' + html.escape(str(est))
-        ) in html_output
+        assert html.escape(str(est))[:44] in html_output
 
     # low level estimators do not show changes
     with config_context(print_changed_only=True):
@@ -217,18 +216,19 @@ def test_estimator_html_repr_pipeline():
             assert f"{html.escape(name)}" in html_output
 
         pca = feat_u.transformer_list[0][1]
-        assert f"{html.escape(str(pca))}" in html_output
+
+        assert html.escape(str(pca)) in html_output
 
         tsvd = feat_u.transformer_list[1][1]
         first = tsvd["first"]
         select = tsvd["select"]
-        assert f"{html.escape(str(first))}" in html_output
-        assert f"{html.escape(str(select))}" in html_output
+        assert html.escape(str(first)) in html_output
+        assert html.escape(str(select)) in html_output
 
         # voting classifier
         for name, est in clf.estimators:
-            assert f"{html.escape(name)}" in html_output
-            assert f"{html.escape(str(est))}" in html_output
+            assert html.escape(name) in html_output
+            assert html.escape(str(est)) in html_output
 
     # verify that prefers-color-scheme is implemented
     assert "prefers-color-scheme" in html_output
@@ -248,7 +248,7 @@ def test_stacking_classifier(final_estimator):
     # If final_estimator's default changes from LogisticRegression
     # this should be updated
     if final_estimator is None:
-        assert "LogisticRegression(" in html_output
+        assert "LogisticRegression" in html_output
     else:
         assert final_estimator.__class__.__name__ in html_output
 
@@ -431,7 +431,7 @@ def test_html_documentation_link_mixin_sklearn(mock_version):
     """
 
     # mock the `__version__` where the mixin is located
-    with patch("sklearn.utils._estimator_html_repr.__version__", mock_version):
+    with patch("sklearn.utils._repr_html.base.__version__", mock_version):
         mixin = _HTMLDocumentationLinkMixin()
 
         assert mixin._doc_link_module == "sklearn"
@@ -608,3 +608,9 @@ def test_function_transformer_show_caption(func, expected_name):
     )
     re_compiled = re.compile(p)
     assert re_compiled.search(html_output)
+
+
+def test_estimator_html_repr_table():
+    """Check that we add the table of parameters in the HTML representation."""
+    est = LogisticRegression(C=10.0, fit_intercept=False)
+    assert "parameters-table" in estimator_html_repr(est)
diff --git a/sklearn/utils/_repr_html/tests/test_params.py b/sklearn/utils/_repr_html/tests/test_params.py
new file mode 100644
index 0000000000000..dd1c7dfb9aff7
--- /dev/null
+++ b/sklearn/utils/_repr_html/tests/test_params.py
@@ -0,0 +1,74 @@
+import pytest
+
+from sklearn import config_context
+from sklearn.utils._repr_html.params import ParamsDict, _params_html_repr, _read_params
+
+
+def test_params_dict_content():
+    """Check the behavior of the ParamsDict class."""
+    params = ParamsDict({"a": 1, "b": 2})
+    assert params["a"] == 1
+    assert params["b"] == 2
+    assert params.non_default == ()
+
+    params = ParamsDict({"a": 1, "b": 2}, non_default=("a",))
+    assert params["a"] == 1
+    assert params["b"] == 2
+    assert params.non_default == ("a",)
+
+
+def test_params_dict_repr_html_():
+    params = ParamsDict({"a": 1, "b": 2}, non_default=("a",))
+    out = params._repr_html_()
+    assert "Parameters" in out
+
+    with config_context(display="text"):
+        msg = "_repr_html_ is only defined when"
+        with pytest.raises(AttributeError, match=msg):
+            params._repr_html_()
+
+
+def test_params_dict_repr_mimebundle():
+    params = ParamsDict({"a": 1, "b": 2}, non_default=("a",))
+    out = params._repr_mimebundle_()
+
+    assert "text/plain" in out
+    assert "text/html" in out
+    assert "Parameters" in out["text/html"]
+    assert out["text/plain"] == "{'a': 1, 'b': 2}"
+
+    with config_context(display="text"):
+        out = params._repr_mimebundle_()
+        assert "text/plain" in out
+        assert "text/html" not in out
+
+
+def test_read_params():
+    """Check the behavior of the `_read_params` function."""
+    out = _read_params("a", 1, tuple())
+    assert out["param_type"] == "default"
+    assert out["param_name"] == "a"
+    assert out["param_value"] == "1"
+
+    # check non-default parameters
+    out = _read_params("a", 1, ("a",))
+    assert out["param_type"] == "user-set"
+    assert out["param_name"] == "a"
+    assert out["param_value"] == "1"
+
+    # check that we escape html tags
+    tag_injection = ""
+    out = _read_params("a", tag_injection, tuple())
+    assert (
+        out["param_value"]
+        == ""<script>alert('xss')</script>""
+    )
+    assert out["param_name"] == "a"
+    assert out["param_type"] == "default"
+
+
+def test_params_html_repr():
+    """Check returned HTML template"""
+    params = ParamsDict({"a": 1, "b": 2})
+    assert "parameters-table" in _params_html_repr(params)
+    assert "estimator-table" in _params_html_repr(params)

From a51ccb8701c796254dd1a693d5ab4ad0fec32625 Mon Sep 17 00:00:00 2001
From: omahs <73983677+omahs@users.noreply.github.com>
Date: Wed, 21 May 2025 16:29:56 +0200
Subject: [PATCH 35/64] DOC Fix various typos in documentation and comments
 (#31404)

---
 doc/modules/ensemble.rst                                  | 4 ++--
 examples/bicluster/plot_spectral_biclustering.py          | 2 +-
 examples/cluster/plot_agglomerative_clustering_metrics.py | 2 +-
 examples/svm/plot_svm_kernels.py                          | 2 +-
 sklearn/_loss/_loss.pyx.tp                                | 2 +-
 sklearn/preprocessing/_function_transformer.py            | 2 +-
 sklearn/utils/fixes.py                                    | 2 +-
 sklearn/utils/tests/test_array_api.py                     | 2 +-
 8 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index f0f14c60e4867..6b0fc93e437ff 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -308,7 +308,7 @@ values.
   all of the :math:`2^{K - 1} - 1` partitions, where :math:`K` is the number of
   categories. This can quickly become prohibitive when :math:`K` is large.
   Fortunately, since gradient boosting trees are always regression trees (even
-  for classification problems), there exist a faster strategy that can yield
+  for classification problems), there exists a faster strategy that can yield
   equivalent splits. First, the categories of a feature are sorted according to
   the variance of the target, for each category `k`. Once the categories are
   sorted, one can consider *continuous partitions*, i.e. treat the categories
@@ -1587,7 +1587,7 @@ Note that it is also possible to get the output of the stacked
 
 In practice, a stacking predictor predicts as good as the best predictor of the
 base layer and even sometimes outperforms it by combining the different
-strengths of the these predictors. However, training a stacking predictor is
+strengths of these predictors. However, training a stacking predictor is
 computationally expensive.
 
 .. note::
diff --git a/examples/bicluster/plot_spectral_biclustering.py b/examples/bicluster/plot_spectral_biclustering.py
index 469c3c71e17c6..86245325ae493 100644
--- a/examples/bicluster/plot_spectral_biclustering.py
+++ b/examples/bicluster/plot_spectral_biclustering.py
@@ -26,7 +26,7 @@
 # --------------------
 # We generate the sample data using the
 # :func:`~sklearn.datasets.make_checkerboard` function. Each pixel within
-# `shape=(300, 300)` represents with it's color a value from a uniform
+# `shape=(300, 300)` represents with its color a value from a uniform
 # distribution. The noise is added from a normal distribution, where the value
 # chosen for `noise` is the standard deviation.
 #
diff --git a/examples/cluster/plot_agglomerative_clustering_metrics.py b/examples/cluster/plot_agglomerative_clustering_metrics.py
index c565a5859d093..dbf929d9576e1 100644
--- a/examples/cluster/plot_agglomerative_clustering_metrics.py
+++ b/examples/cluster/plot_agglomerative_clustering_metrics.py
@@ -18,7 +18,7 @@
 
 We add observation noise to these waveforms. We generate very sparse
 noise: only 6% of the time points contain noise. As a result, the
-l1 norm of this noise (ie "cityblock" distance) is much smaller than it's
+l1 norm of this noise (ie "cityblock" distance) is much smaller than its
 l2 norm ("euclidean" distance). This can be seen on the inter-class
 distance matrices: the values on the diagonal, that characterize the
 spread of the class, are much bigger for the Euclidean distance than for
diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py
index df29d198abcbc..d01f049dbe0b4 100644
--- a/examples/svm/plot_svm_kernels.py
+++ b/examples/svm/plot_svm_kernels.py
@@ -255,7 +255,7 @@ def plot_training_data_with_decision_boundary(
 # that may not generalize well to unseen data. From this example it becomes
 # obvious, that the sigmoid kernel has very specific use cases, when dealing
 # with data that exhibits a sigmoidal shape. In this example, careful fine
-# tuning might find more generalizable decision boundaries. Because of it's
+# tuning might find more generalizable decision boundaries. Because of its
 # specificity, the sigmoid kernel is less commonly used in practice compared to
 # other kernels.
 #
diff --git a/sklearn/_loss/_loss.pyx.tp b/sklearn/_loss/_loss.pyx.tp
index 6054d4c9472ca..44d5acd530a7f 100644
--- a/sklearn/_loss/_loss.pyx.tp
+++ b/sklearn/_loss/_loss.pyx.tp
@@ -121,7 +121,7 @@ doc_HalfTweedieLoss = (
         - y_true * exp((1-p) * raw_prediction) / (1-p)
 
     Notes:
-    - Poisson with p=1 and and Gamma with p=2 have different terms dropped such
+    - Poisson with p=1 and Gamma with p=2 have different terms dropped such
       that cHalfTweedieLoss is not continuous in p=power at p=1 and p=2.
     - While the Tweedie distribution only exists for p<=0 or p>=1, the range
       0
Date: Wed, 21 May 2025 17:33:21 +0200
Subject: [PATCH 36/64] TST use global_random_seed in
 sklearn/decomposition/tests/test_incremental_pca.py (#31250)

---
 .../tests/test_incremental_pca.py             | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/sklearn/decomposition/tests/test_incremental_pca.py b/sklearn/decomposition/tests/test_incremental_pca.py
index 6bca13d0ad627..c4ea1c222901c 100644
--- a/sklearn/decomposition/tests/test_incremental_pca.py
+++ b/sklearn/decomposition/tests/test_incremental_pca.py
@@ -87,9 +87,9 @@ def test_incremental_pca_sparse(sparse_container):
         ipca.partial_fit(X_sparse)
 
 
-def test_incremental_pca_check_projection():
+def test_incremental_pca_check_projection(global_random_seed):
     # Test that the projection of data is correct.
-    rng = np.random.RandomState(1999)
+    rng = np.random.RandomState(global_random_seed)
     n, p = 100, 3
     X = rng.randn(n, p) * 0.1
     X[:10] += np.array([3, 4, 5])
@@ -108,9 +108,9 @@ def test_incremental_pca_check_projection():
     assert_almost_equal(np.abs(Yt[0][0]), 1.0, 1)
 
 
-def test_incremental_pca_inverse():
+def test_incremental_pca_inverse(global_random_seed):
     # Test that the projection of data can be inverted.
-    rng = np.random.RandomState(1999)
+    rng = np.random.RandomState(global_random_seed)
     n, p = 50, 3
     X = rng.randn(n, p)  # spherical data
     X[:, 1] *= 0.00001  # make middle component relatively small
@@ -217,9 +217,9 @@ def test_incremental_pca_num_features_change():
         ipca.partial_fit(X2)
 
 
-def test_incremental_pca_batch_signs():
+def test_incremental_pca_batch_signs(global_random_seed):
     # Test that components_ sign is stable over batch sizes.
-    rng = np.random.RandomState(1999)
+    rng = np.random.RandomState(global_random_seed)
     n_samples = 100
     n_features = 3
     X = rng.randn(n_samples, n_features)
@@ -254,9 +254,9 @@ def test_incremental_pca_partial_fit_small_batch():
     assert_allclose(pca.components_, pipca.components_, atol=1e-3)
 
 
-def test_incremental_pca_batch_values():
+def test_incremental_pca_batch_values(global_random_seed):
     # Test that components_ values are stable over batch sizes.
-    rng = np.random.RandomState(1999)
+    rng = np.random.RandomState(global_random_seed)
     n_samples = 100
     n_features = 3
     X = rng.randn(n_samples, n_features)
@@ -286,9 +286,9 @@ def test_incremental_pca_batch_rank():
         assert_allclose_dense_sparse(components_i, components_j)
 
 
-def test_incremental_pca_partial_fit():
+def test_incremental_pca_partial_fit(global_random_seed):
     # Test that fit and partial_fit get equivalent results.
-    rng = np.random.RandomState(1999)
+    rng = np.random.RandomState(global_random_seed)
     n, p = 50, 3
     X = rng.randn(n, p)  # spherical data
     X[:, 1] *= 0.00001  # make middle component relatively small
@@ -316,9 +316,9 @@ def test_incremental_pca_against_pca_iris():
     assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1)
 
 
-def test_incremental_pca_against_pca_random_data():
+def test_incremental_pca_against_pca_random_data(global_random_seed):
     # Test that IncrementalPCA and PCA are approximate (to a sign flip).
-    rng = np.random.RandomState(1999)
+    rng = np.random.RandomState(global_random_seed)
     n_samples = 100
     n_features = 3
     X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features)
@@ -348,10 +348,10 @@ def test_explained_variances():
         assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec)
 
 
-def test_singular_values():
+def test_singular_values(global_random_seed):
     # Check that the IncrementalPCA output has the correct singular values
 
-    rng = np.random.RandomState(0)
+    rng = np.random.RandomState(global_random_seed)
     n_samples = 1000
     n_features = 100
 
@@ -360,7 +360,7 @@ def test_singular_values():
     )
 
     pca = PCA(n_components=10, svd_solver="full", random_state=rng).fit(X)
-    ipca = IncrementalPCA(n_components=10, batch_size=100).fit(X)
+    ipca = IncrementalPCA(n_components=10, batch_size=150).fit(X)
     assert_array_almost_equal(pca.singular_values_, ipca.singular_values_, 2)
 
     # Compare to the Frobenius norm
@@ -382,7 +382,7 @@ def test_singular_values():
     )
 
     # Set the singular values and see what we get back
-    rng = np.random.RandomState(0)
+    rng = np.random.RandomState(global_random_seed)
     n_samples = 100
     n_features = 110
 

From 41aa1ebc360313caf526a62f1dc68fdc57baa9ac Mon Sep 17 00:00:00 2001
From: Christian Lorentzen 
Date: Thu, 22 May 2025 10:15:47 +0200
Subject: [PATCH 37/64] MNT cleaner Cython coordinate descent in _cd_fast.pyx
 (#31372)

---
 sklearn/linear_model/_cd_fast.pyx | 144 ++++++++++++++++--------------
 1 file changed, 76 insertions(+), 68 deletions(-)

diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx
index c4c530d907e26..6f17622bc4789 100644
--- a/sklearn/linear_model/_cd_fast.pyx
+++ b/sklearn/linear_model/_cd_fast.pyx
@@ -83,6 +83,21 @@ cdef floating diff_abs_max(int n, const floating* a, floating* b) noexcept nogil
     return m
 
 
+message_conv = (
+    "Objective did not converge. You might want to increase "
+    "the number of iterations, check the scale of the "
+    "features or consider increasing regularisation."
+)
+
+
+message_ridge = (
+    "Linear regression models with a zero l1 penalization "
+    "strength are more efficiently fitted using one of the "
+    "solvers implemented in "
+    "sklearn.linear_model.Ridge/RidgeCV instead."
+)
+
+
 def enet_coordinate_descent(
     floating[::1] w,
     floating alpha,
@@ -141,7 +156,7 @@ def enet_coordinate_descent(
     cdef floating R_norm2
     cdef floating w_norm2
     cdef floating l1_norm
-    cdef floating const
+    cdef floating const_
     cdef floating A_norm2
     cdef unsigned int ii
     cdef unsigned int n_iter = 0
@@ -227,19 +242,18 @@ def enet_coordinate_descent(
                 w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1)
 
                 if (dual_norm_XtA > alpha):
-                    const = alpha / dual_norm_XtA
-                    A_norm2 = R_norm2 * (const ** 2)
+                    const_ = alpha / dual_norm_XtA
+                    A_norm2 = R_norm2 * (const_ ** 2)
                     gap = 0.5 * (R_norm2 + A_norm2)
                 else:
-                    const = 1.0
+                    const_ = 1.0
                     gap = R_norm2
 
                 l1_norm = _asum(n_features, &w[0], 1)
 
-                # np.dot(R.T, y)
                 gap += (alpha * l1_norm
-                        - const * _dot(n_samples, &R[0], 1, &y[0], 1)
-                        + 0.5 * beta * (1 + const ** 2) * (w_norm2))
+                        - const_ * _dot(n_samples, &R[0], 1, &y[0], 1)  # np.dot(R.T, y)
+                        + 0.5 * beta * (1 + const_ ** 2) * (w_norm2))
 
                 if gap < tol:
                     # return if we reached desired tolerance
@@ -249,18 +263,11 @@ def enet_coordinate_descent(
             # for/else, runs if for doesn't end with a `break`
             with gil:
                 message = (
-                    "Objective did not converge. You might want to increase "
-                    "the number of iterations, check the scale of the "
-                    "features or consider increasing regularisation. "
-                    f"Duality gap: {gap:.3e}, tolerance: {tol:.3e}"
+                    message_conv +
+                    f" Duality gap: {gap:.3e}, tolerance: {tol:.3e}"
                 )
                 if alpha < np.finfo(np.float64).eps:
-                    message += (
-                        " Linear regression models with null weight for the "
-                        "l1 regularization term are more efficiently fitted "
-                        "using one of the solvers implemented in "
-                        "sklearn.linear_model.Ridge/RidgeCV instead."
-                    )
+                    message += "\n" + message_ridge
                 warnings.warn(message, ConvergenceWarning)
 
     return np.asarray(w), gap, tol, n_iter + 1
@@ -313,53 +320,50 @@ def sparse_enet_coordinate_descent(
     # that every calculation results as if we had rescaled y and X (and therefore also
     # X_mean) by sqrt(sample_weight) without actually calculating the square root.
     # We work with:
-    #     yw = sample_weight
+    #     yw = sample_weight * y
     #     R = sample_weight * residual
     #     norm_cols_X = np.sum(sample_weight * (X - X_mean)**2, axis=0)
 
+    if floating is float:
+        dtype = np.float32
+    else:
+        dtype = np.float64
+
     # get the data information into easy vars
     cdef unsigned int n_samples = y.shape[0]
     cdef unsigned int n_features = w.shape[0]
 
     # compute norms of the columns of X
-    cdef unsigned int ii
-    cdef floating[:] norm_cols_X
-
-    cdef unsigned int startptr = X_indptr[0]
-    cdef unsigned int endptr
+    cdef floating[:] norm_cols_X = np.zeros(n_features, dtype=dtype)
 
     # initial value of the residuals
     # R = y - Zw, weighted version R = sample_weight * (y - Zw)
     cdef floating[::1] R
-    cdef floating[::1] XtA
+    cdef floating[::1] XtA = np.empty(n_features, dtype=dtype)
     cdef const floating[::1] yw
 
-    if floating is float:
-        dtype = np.float32
-    else:
-        dtype = np.float64
-
-    norm_cols_X = np.zeros(n_features, dtype=dtype)
-    XtA = np.zeros(n_features, dtype=dtype)
-
     cdef floating tmp
     cdef floating w_ii
     cdef floating d_w_max
     cdef floating w_max
     cdef floating d_w_ii
+    cdef floating gap = tol + 1.0
+    cdef floating d_w_tol = tol
+    cdef floating dual_norm_XtA
     cdef floating X_mean_ii
     cdef floating R_sum = 0.0
     cdef floating R_norm2
     cdef floating w_norm2
-    cdef floating A_norm2
     cdef floating l1_norm
+    cdef floating const_
+    cdef floating A_norm2
     cdef floating normalize_sum
-    cdef floating gap = tol + 1.0
-    cdef floating d_w_tol = tol
-    cdef floating dual_norm_XtA
+    cdef unsigned int ii
     cdef unsigned int jj
     cdef unsigned int n_iter = 0
     cdef unsigned int f_iter
+    cdef unsigned int startptr = X_indptr[0]
+    cdef unsigned int endptr
     cdef uint32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX)
     cdef uint32_t* rand_r_state = &rand_r_state_seed
     cdef bint center = False
@@ -380,6 +384,7 @@ def sparse_enet_coordinate_descent(
                 center = True
                 break
 
+        # R = y - np.dot(X, w)
         for ii in range(n_features):
             X_mean_ii = X_mean[ii]
             endptr = X_indptr[ii + 1]
@@ -396,6 +401,7 @@ def sparse_enet_coordinate_descent(
                     for jj in range(n_samples):
                         R[jj] += X_mean_ii * w_ii
             else:
+                # R = sw * (y - np.dot(X, w))
                 for jj in range(startptr, endptr):
                     tmp = sample_weight[X_indices[jj]]
                     # second term will be subtracted by loop over range(n_samples)
@@ -526,21 +532,18 @@ def sparse_enet_coordinate_descent(
                 # w_norm2 = np.dot(w, w)
                 w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1)
                 if (dual_norm_XtA > alpha):
-                    const = alpha / dual_norm_XtA
-                    A_norm2 = R_norm2 * const**2
+                    const_ = alpha / dual_norm_XtA
+                    A_norm2 = R_norm2 * const_**2
                     gap = 0.5 * (R_norm2 + A_norm2)
                 else:
-                    const = 1.0
+                    const_ = 1.0
                     gap = R_norm2
 
                 l1_norm = _asum(n_features, &w[0], 1)
 
-                gap += (alpha * l1_norm - const * _dot(
-                            n_samples,
-                            &R[0], 1,
-                            &y[0], 1
-                            )
-                        + 0.5 * beta * (1 + const ** 2) * w_norm2)
+                gap += (alpha * l1_norm
+                        - const_ * _dot(n_samples, &R[0], 1, &y[0], 1)  # np.dot(R.T, y)
+                        + 0.5 * beta * (1 + const_ ** 2) * w_norm2)
 
                 if gap < tol:
                     # return if we reached desired tolerance
@@ -549,10 +552,13 @@ def sparse_enet_coordinate_descent(
         else:
             # for/else, runs if for doesn't end with a `break`
             with gil:
-                warnings.warn("Objective did not converge. You might want to "
-                              "increase the number of iterations. Duality "
-                              "gap: {}, tolerance: {}".format(gap, tol),
-                              ConvergenceWarning)
+                message = (
+                    message_conv +
+                    f" Duality gap: {gap:.3e}, tolerance: {tol:.3e}"
+                )
+                if alpha < np.finfo(np.float64).eps:
+                    message += "\n" + message_ridge
+                warnings.warn(message, ConvergenceWarning)
 
     return np.asarray(w), gap, tol, n_iter + 1
 
@@ -702,19 +708,19 @@ def enet_coordinate_descent_gram(
                 w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1)
 
                 if (dual_norm_XtA > alpha):
-                    const = alpha / dual_norm_XtA
-                    A_norm2 = R_norm2 * (const ** 2)
+                    const_ = alpha / dual_norm_XtA
+                    A_norm2 = R_norm2 * (const_ ** 2)
                     gap = 0.5 * (R_norm2 + A_norm2)
                 else:
-                    const = 1.0
+                    const_ = 1.0
                     gap = R_norm2
 
                 # The call to asum is equivalent to the L1 norm of w
                 gap += (
                     alpha * _asum(n_features, &w[0], 1)
-                    - const * y_norm2
-                    + const * q_dot_w
-                    + 0.5 * beta * (1 + const ** 2) * w_norm2
+                    - const_ * y_norm2
+                    + const_ * q_dot_w
+                    + 0.5 * beta * (1 + const_ ** 2) * w_norm2
                 )
 
                 if gap < tol:
@@ -724,10 +730,11 @@ def enet_coordinate_descent_gram(
         else:
             # for/else, runs if for doesn't end with a `break`
             with gil:
-                warnings.warn("Objective did not converge. You might want to "
-                              "increase the number of iterations. Duality "
-                              "gap: {}, tolerance: {}".format(gap, tol),
-                              ConvergenceWarning)
+                message = (
+                    message_conv +
+                    f" Duality gap: {gap:.3e}, tolerance: {tol:.3e}"
+                )
+                warnings.warn(message, ConvergenceWarning)
 
     return np.asarray(w), gap, tol, n_iter + 1
 
@@ -921,11 +928,11 @@ def enet_coordinate_descent_multi_task(
                 R_norm = _nrm2(n_samples * n_tasks, &R[0, 0], 1)
                 w_norm = _nrm2(n_features * n_tasks, &W[0, 0], 1)
                 if (dual_norm_XtA > l1_reg):
-                    const = l1_reg / dual_norm_XtA
-                    A_norm = R_norm * const
+                    const_ = l1_reg / dual_norm_XtA
+                    A_norm = R_norm * const_
                     gap = 0.5 * (R_norm ** 2 + A_norm ** 2)
                 else:
-                    const = 1.0
+                    const_ = 1.0
                     gap = R_norm ** 2
 
                 # ry_sum = np.sum(R * y)
@@ -938,8 +945,8 @@ def enet_coordinate_descent_multi_task(
 
                 gap += (
                     l1_reg * l21_norm
-                    - const * ry_sum
-                    + 0.5 * l2_reg * (1 + const ** 2) * (w_norm ** 2)
+                    - const_ * ry_sum
+                    + 0.5 * l2_reg * (1 + const_ ** 2) * (w_norm ** 2)
                 )
 
                 if gap <= tol:
@@ -948,9 +955,10 @@ def enet_coordinate_descent_multi_task(
         else:
             # for/else, runs if for doesn't end with a `break`
             with gil:
-                warnings.warn("Objective did not converge. You might want to "
-                              "increase the number of iterations. Duality "
-                              "gap: {}, tolerance: {}".format(gap, tol),
-                              ConvergenceWarning)
+                message = (
+                    message_conv +
+                    f" Duality gap: {gap:.3e}, tolerance: {tol:.3e}"
+                )
+                warnings.warn(message, ConvergenceWarning)
 
     return np.asarray(W), gap, tol, n_iter + 1

From cb730ca2ae334e64a22ae9f8f1afb94f58c3d9af Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= 
Date: Thu, 22 May 2025 10:51:43 +0200
Subject: [PATCH 38/64] CI Avoid joblib 1.5.0 in Pyodide (#31402)

---
 .github/workflows/emscripten.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/emscripten.yml b/.github/workflows/emscripten.yml
index 47e54f6125638..6ed68496de8b2 100644
--- a/.github/workflows/emscripten.yml
+++ b/.github/workflows/emscripten.yml
@@ -72,7 +72,9 @@ jobs:
           CIBW_PLATFORM: pyodide
           SKLEARN_SKIP_OPENMP_TEST: "true"
           SKLEARN_SKIP_NETWORK_TESTS: 1
-          CIBW_TEST_REQUIRES: "pytest pandas"
+          # Temporary work-around to avoid joblib 1.5.0 until there is a joblib
+          # release with https://github.com/joblib/joblib/pull/1721
+          CIBW_TEST_REQUIRES: "pytest pandas joblib!=1.5.0"
           # -s pytest argument is needed to avoid an issue in pytest output capturing with Pyodide
           CIBW_TEST_COMMAND: "python -m pytest -svra --pyargs sklearn --durations 20 --showlocals"
 

From 8d3da93ee5712b613901bf531ffcc9e9b3ba82ed Mon Sep 17 00:00:00 2001
From: Radovenchyk 
Date: Thu, 22 May 2025 13:54:59 +0300
Subject: [PATCH 39/64] DOC Fix wheel builder badge on README (#31409)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Loïc Estève 
---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 4f4741a090dee..e48c192fac043 100644
--- a/README.rst
+++ b/README.rst
@@ -11,7 +11,7 @@
 .. |Codecov| image:: https://codecov.io/gh/scikit-learn/scikit-learn/branch/main/graph/badge.svg?token=Pk8G9gg3y9
    :target: https://codecov.io/gh/scikit-learn/scikit-learn
 
-.. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule
+.. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/actions/workflows/wheels.yml/badge.svg?event=schedule
    :target: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule
 
 .. |Ruff| image:: https://img.shields.io/badge/code%20style-ruff-000000.svg

From 247b72ce03e8114f248572603a1a83da7b73fbf8 Mon Sep 17 00:00:00 2001
From: Lucy Liu 
Date: Fri, 23 May 2025 18:57:23 +1000
Subject: [PATCH 40/64] DOC Minor updates to `OPTICS` docstring (#31363)

---
 sklearn/cluster/_optics.py | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py
index 0cd32023de46c..4a1a80c9065c2 100644
--- a/sklearn/cluster/_optics.py
+++ b/sklearn/cluster/_optics.py
@@ -34,19 +34,21 @@ class OPTICS(ClusterMixin, BaseEstimator):
     """Estimate clustering structure from vector array.
 
     OPTICS (Ordering Points To Identify the Clustering Structure), closely
-    related to DBSCAN, finds core sample of high density and expands clusters
-    from them [1]_. Unlike DBSCAN, keeps cluster hierarchy for a variable
+    related to DBSCAN, finds core samples of high density and expands clusters
+    from them [1]_. Unlike DBSCAN, it keeps cluster hierarchy for a variable
     neighborhood radius. Better suited for usage on large datasets than the
-    current sklearn implementation of DBSCAN.
+    current scikit-learn implementation of DBSCAN.
 
-    Clusters are then extracted using a DBSCAN-like method
-    (cluster_method = 'dbscan') or an automatic
+    Clusters are then extracted from the cluster-order using a
+    DBSCAN-like method (cluster_method = 'dbscan') or an automatic
     technique proposed in [1]_ (cluster_method = 'xi').
 
     This implementation deviates from the original OPTICS by first performing
-    k-nearest-neighborhood searches on all points to identify core sizes, then
-    computing only the distances to unprocessed points when constructing the
-    cluster order. Note that we do not employ a heap to manage the expansion
+    k-nearest-neighborhood searches on all points to identify core sizes of
+    all points (instead of computing neighbors while looping through points).
+    Reachability distances to only unprocessed points are then computed, to
+    construct the cluster order, similar to the original OPTICS.
+    Note that we do not employ a heap to manage the expansion
     candidates, so the time complexity will be O(n^2).
 
     Read more in the :ref:`User Guide `.
@@ -68,9 +70,9 @@ class OPTICS(ClusterMixin, BaseEstimator):
 
     metric : str or callable, default='minkowski'
         Metric to use for distance computation. Any metric from scikit-learn
-        or scipy.spatial.distance can be used.
+        or :mod:`scipy.spatial.distance` can be used.
 
-        If metric is a callable function, it is called on each
+        If `metric` is a callable function, it is called on each
         pair of instances (rows) and the resulting value recorded. The callable
         should take two arrays as input and return one value indicating the
         distance between them. This works for Scipy's metrics, but is less
@@ -90,8 +92,7 @@ class OPTICS(ClusterMixin, BaseEstimator):
           'yule']
 
         Sparse matrices are only supported by scikit-learn metrics.
-        See the documentation for scipy.spatial.distance for details on these
-        metrics.
+        See :mod:`scipy.spatial.distance` for details on these metrics.
 
         .. note::
            `'kulsinski'` is deprecated from SciPy 1.9 and will be removed in SciPy 1.11.
@@ -105,9 +106,9 @@ class OPTICS(ClusterMixin, BaseEstimator):
     metric_params : dict, default=None
         Additional keyword arguments for the metric function.
 
-    cluster_method : str, default='xi'
+    cluster_method : {'xi', 'dbscan'}, default='xi'
         The extraction method used to extract clusters using the calculated
-        reachability and ordering. Possible values are "xi" and "dbscan".
+        reachability and ordering.
 
     eps : float, default=None
         The maximum distance between two samples for one to be considered as

From cdeadf4efdbb39115871c39bf7aec6fb11eda732 Mon Sep 17 00:00:00 2001
From: Christian Lorentzen 
Date: Fri, 23 May 2025 20:01:41 +0200
Subject: [PATCH 41/64] DOC fixes for LogisticRegression newton-cholesky and
 multiclass (#31410)

Co-authored-by: Olivier Grisel 
---
 doc/modules/linear_model.rst      | 22 ++++++++++++----------
 sklearn/linear_model/_logistic.py | 20 +++++++++++---------
 2 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
index 007afdc592c29..b575e4597b6fa 100644
--- a/doc/modules/linear_model.rst
+++ b/doc/modules/linear_model.rst
@@ -1022,7 +1022,7 @@ The following table summarizes the penalties and multinomial multiclass supporte
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
 | **Penalties**                | **'lbfgs'** | **'liblinear'** | **'newton-cg'** | **'newton-cholesky'** | **'sag'** | **'saga'** |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| L2 penalty                   |     yes     |       no        |       yes       |     no                |    yes    |    yes     |
+| L2 penalty                   |     yes     |       yes       |       yes       |     yes               |    yes    |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
 | L1 penalty                   |     no      |       yes       |       no        |     no                |    no     |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
@@ -1032,7 +1032,7 @@ The following table summarizes the penalties and multinomial multiclass supporte
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
 | **Multiclass support**       |                                                                                                  |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| multinomial multiclass       |     yes     |       no        |       yes       |     no                |    yes    |    yes     |
+| multinomial multiclass       |     yes     |       no        |       yes       |     yes               |    yes    |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
 | **Behaviors**                |                                                                                                  |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
@@ -1043,8 +1043,11 @@ The following table summarizes the penalties and multinomial multiclass supporte
 | Robust to unscaled datasets  |     yes     |       yes       |       yes       |     yes               |    no     |    no      |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
 
-The "lbfgs" solver is used by default for its robustness. For large datasets
-the "saga" solver is usually faster.
+The "lbfgs" solver is used by default for its robustness. For
+`n_samples >> n_features`, "newton-cholesky" is a good choice and can reach high
+precision (tiny `tol` values). For large datasets
+the "saga" solver is usually faster (than "lbfgs"), in particular for low precision
+(high `tol`).
 For large dataset, you may also consider using :class:`SGDClassifier`
 with `loss="log_loss"`, which might be even faster but requires more tuning.
 
@@ -1101,13 +1104,12 @@ zero, is likely to be an underfit, bad model and you are advised to set
     scaled datasets and on datasets with one-hot encoded categorical features with rare
     categories.
 
-  * The "newton-cholesky" solver is an exact Newton solver that calculates the hessian
+  * The "newton-cholesky" solver is an exact Newton solver that calculates the Hessian
     matrix and solves the resulting linear system. It is a very good choice for
-    `n_samples` >> `n_features`, but has a few shortcomings: Only :math:`\ell_2`
-    regularization is supported. Furthermore, because the hessian matrix is explicitly
-    computed, the memory usage has a quadratic dependency on `n_features` as well as on
-    `n_classes`. As a consequence, only the one-vs-rest scheme is implemented for the
-    multiclass case.
+    `n_samples` >> `n_features` and can reach high precision (tiny values of `tol`),
+    but has a few shortcomings: Only :math:`\ell_2` regularization is supported.
+    Furthermore, because the Hessian matrix is explicitly computed, the memory usage
+    has a quadratic dependency on `n_features` as well as on `n_classes`.
 
   For a comparison of some of these solvers, see [9]_.
 
diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index 89a17b7fffe0d..f0c97268c612d 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -337,7 +337,7 @@ def _logistic_regression_path(
 
     else:
         if solver in ["sag", "saga", "lbfgs", "newton-cg", "newton-cholesky"]:
-            # SAG, lbfgs, newton-cg and newton-cg multinomial solvers need
+            # SAG, lbfgs, newton-cg and newton-cholesky multinomial solvers need
             # LabelEncoder, not LabelBinarizer, i.e. y as a 1d-array of integers.
             # LabelEncoder also saves memory compared to LabelBinarizer, especially
             # when n_classes is large.
@@ -837,9 +837,9 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
     the L2 penalty. The Elastic-Net regularization is only supported by the
     'saga' solver.
 
-    For :term:`multiclass` problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'
-    handle multinomial loss. 'liblinear' and 'newton-cholesky' only handle binary
-    classification but can be extended to handle multiclass by using
+    For :term:`multiclass` problems, all solvers but 'liblinear' optimize the
+    (penalized) multinomial loss. 'liblinear' only handle binary classification but can
+    be extended to handle multiclass by using
     :class:`~sklearn.multiclass.OneVsRestClassifier`.
 
     Read more in the :ref:`User Guide `.
@@ -957,13 +957,14 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
            summarizing solver/penalty supports.
 
         .. versionadded:: 0.17
-           Stochastic Average Gradient descent solver.
+           Stochastic Average Gradient (SAG) descent solver. Multinomial support in
+           version 0.18.
         .. versionadded:: 0.19
            SAGA solver.
         .. versionchanged:: 0.22
-            The default solver changed from 'liblinear' to 'lbfgs' in 0.22.
+           The default solver changed from 'liblinear' to 'lbfgs' in 0.22.
         .. versionadded:: 1.2
-           newton-cholesky solver.
+           newton-cholesky solver. Multinomial support in version 1.6.
 
     max_iter : int, default=100
         Maximum number of iterations taken for the solvers to converge.
@@ -1597,11 +1598,12 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima
            a scaler from :mod:`sklearn.preprocessing`.
 
         .. versionadded:: 0.17
-           Stochastic Average Gradient descent solver.
+           Stochastic Average Gradient (SAG) descent solver. Multinomial support in
+           version 0.18.
         .. versionadded:: 0.19
            SAGA solver.
         .. versionadded:: 1.2
-           newton-cholesky solver.
+           newton-cholesky solver. Multinomial support in version 1.6.
 
     tol : float, default=1e-4
         Tolerance for stopping criteria.

From 0d2fec340c09776090f83d11ad284c8b02f9a2c1 Mon Sep 17 00:00:00 2001
From: "Christine P. Chai" 
Date: Sat, 24 May 2025 08:42:25 -0700
Subject: [PATCH 42/64] DOC Revise a math equation to incorporate text (#31421)

---
 doc/modules/model_evaluation.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
index cf168295a6024..c304966fccdb2 100644
--- a/doc/modules/model_evaluation.rst
+++ b/doc/modules/model_evaluation.rst
@@ -1880,7 +1880,7 @@ In multilabel classification, the :func:`zero_one_loss` scores a subset as
 one if its labels strictly match the predictions, and as a zero if there
 are any errors.  By default, the function returns the percentage of imperfectly
 predicted subsets.  To get the count of such subsets instead, set
-``normalize`` to ``False``
+``normalize`` to ``False``.
 
 If :math:`\hat{y}_i` is the predicted value of
 the :math:`i`-th sample and :math:`y_i` is the corresponding true value,
@@ -1891,8 +1891,8 @@ then the 0-1 loss :math:`L_{0-1}` is defined as:
    L_{0-1}(y, \hat{y}) = \frac{1}{n_\text{samples}} \sum_{i=0}^{n_\text{samples}-1} 1(\hat{y}_i \not= y_i)
 
 where :math:`1(x)` is the `indicator function
-`_. The zero one
-loss can also be computed as :math:`zero-one loss = 1 - accuracy`.
+`_. The zero-one
+loss can also be computed as :math:`\text{zero-one loss} = 1 - \text{accuracy}`.
 
 
   >>> from sklearn.metrics import zero_one_loss

From 76e27912b41e78a5c97fca86eb7c031520ec8908 Mon Sep 17 00:00:00 2001
From: Adrien Linares <76013394+adlina1@users.noreply.github.com>
Date: Mon, 26 May 2025 04:09:57 +0200
Subject: [PATCH 43/64] DOC Update documentation: Communication section
 improvements (#31420)

---
 README.rst | 44 +++++++++++++++++++++++++++++---------------
 1 file changed, 29 insertions(+), 15 deletions(-)

diff --git a/README.rst b/README.rst
index e48c192fac043..5885bce67baa7 100644
--- a/README.rst
+++ b/README.rst
@@ -176,22 +176,36 @@ Documentation
 Communication
 ~~~~~~~~~~~~~
 
-- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
-- Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
-- Blog: https://blog.scikit-learn.org
-- Calendar: https://blog.scikit-learn.org/calendar/
-- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
-- GitHub Discussions: https://github.com/scikit-learn/scikit-learn/discussions
-- Website: https://scikit-learn.org
-- LinkedIn: https://www.linkedin.com/company/scikit-learn
-- Bluesky: https://bsky.app/profile/scikit-learn.org
-- Mastodon: https://mastodon.social/@sklearn@fosstodon.org
-- YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
-- Facebook: https://www.facebook.com/scikitlearnofficial/
-- Instagram: https://www.instagram.com/scikitlearnofficial/
-- TikTok: https://www.tiktok.com/@scikit.learn
-- Discord: https://discord.gg/h9qyrK8Jc8
+Main Channels
+^^^^^^^^^^^^^
 
+- **Website**: https://scikit-learn.org
+- **Blog**: https://blog.scikit-learn.org
+- **Mailing list**: https://mail.python.org/mailman/listinfo/scikit-learn
+
+Developer & Support
+^^^^^^^^^^^^^^^^^^^^^^
+
+- **GitHub Discussions**: https://github.com/scikit-learn/scikit-learn/discussions
+- **Stack Overflow**: https://stackoverflow.com/questions/tagged/scikit-learn
+- **Discord**: https://discord.gg/h9qyrK8Jc8
+
+Social Media Platforms
+^^^^^^^^^^^^^^^^^^^^^^
+
+- **LinkedIn**: https://www.linkedin.com/company/scikit-learn
+- **YouTube**: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
+- **Facebook**: https://www.facebook.com/scikitlearnofficial/
+- **Instagram**: https://www.instagram.com/scikitlearnofficial/
+- **TikTok**: https://www.tiktok.com/@scikit.learn
+- **Bluesky**: https://bsky.app/profile/scikit-learn.org
+- **Mastodon**: https://mastodon.social/@sklearn@fosstodon.org
+
+Resources
+^^^^^^^^^
+
+- **Calendar**: https://blog.scikit-learn.org/calendar/
+- **Logos & Branding**: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
 
 Citation
 ~~~~~~~~

From d3e01803eec3728ab2c3c9869da5a70e58278793 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dea=20Mar=C3=ADa=20L=C3=A9on?= 
Date: Mon, 26 May 2025 08:26:50 +0200
Subject: [PATCH 44/64] DOC: Add `from_predictions` example and other details
 to `visualizations.rst` (#30825)

---
 doc/visualizations.rst | 92 ++++++++++++++++++++++++++++++++----------
 1 file changed, 71 insertions(+), 21 deletions(-)

diff --git a/doc/visualizations.rst b/doc/visualizations.rst
index 412dfc001fab1..e42be3a6db040 100644
--- a/doc/visualizations.rst
+++ b/doc/visualizations.rst
@@ -8,37 +8,86 @@ Scikit-learn defines a simple API for creating visualizations for machine
 learning. The key feature of this API is to allow for quick plotting and
 visual adjustments without recalculation. We provide `Display` classes that
 expose two methods for creating plots: `from_estimator` and
-`from_predictions`. The `from_estimator` method will take a fitted estimator
-and some data (`X` and `y`) and create a `Display` object. Sometimes, we would
-like to only compute the predictions once and one should use `from_predictions`
-instead. In the following example, we plot a ROC curve for a fitted support
-vector machine:
+`from_predictions`.
+
+The `from_estimator` method generates a `Display` object from a fitted estimator,
+input data (`X`, `y`), and a plot.
+The `from_predictions` method creates a `Display` object from true and predicted
+values (`y_test`, `y_pred`), and a plot.
+
+Using `from_predictions` avoids having to recompute predictions,
+but the user needs to take care that the prediction values passed correspond
+to the `pos_label`. For :term:`predict_proba`, select the column corresponding
+to the `pos_label` class while for :term:`decision_function`, revert the score
+(i.e. multiply by -1) if `pos_label` is not the last class in the
+`classes_` attribute of your estimator.
+
+The `Display` object stores the computed values (e.g., metric values or
+feature importance) required for plotting with Matplotlib. These values are the
+results derived from the raw predictions passed to `from_predictions`, or
+an estimator and `X` passed to `from_estimator`.
+
+Display objects have a plot method that creates a matplotlib plot once the display
+object has been initialized (note that we recommend that display objects are created
+via `from_estimator` or `from_predictions` instead of initialized directly).
+The plot method allows adding to an existing plot by passing the existing plots
+:class:`matplotlib.axes.Axes` to the `ax` parameter.
+
+In the following example, we plot a ROC curve for a fitted Logistic Regression
+model `from_estimator`:
 
 .. plot::
    :context: close-figs
    :align: center
 
     from sklearn.model_selection import train_test_split
-    from sklearn.svm import SVC
+    from sklearn.linear_model import LogisticRegression
     from sklearn.metrics import RocCurveDisplay
-    from sklearn.datasets import load_wine
+    from sklearn.datasets import load_iris
 
-    X, y = load_wine(return_X_y=True)
+    X, y = load_iris(return_X_y=True)
     y = y == 2  # make binary
-    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
-    svc = SVC(random_state=42)
-    svc.fit(X_train, y_train)
+    X_train, X_test, y_train, y_test = train_test_split(
+       X, y, test_size=.8, random_state=42
+    )
+    clf = LogisticRegression(random_state=42, C=.01)
+    clf.fit(X_train, y_train)
 
-    svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
+    clf_disp = RocCurveDisplay.from_estimator(clf, X_test, y_test)
 
-The returned `svc_disp` object allows us to continue using the already computed
-ROC curve for SVC in future plots. In this case, the `svc_disp` is a
-:class:`~sklearn.metrics.RocCurveDisplay` that stores the computed values as
-attributes called `roc_auc`, `fpr`, and `tpr`. Be aware that we could get
-the predictions from the support vector machine and then use `from_predictions`
-instead of `from_estimator`. Next, we train a random forest classifier and plot
-the previously computed ROC curve again by using the `plot` method of the
-`Display` object.
+If you already have the prediction values, you could instead use
+`from_predictions` to do the same thing (and save on compute):
+
+
+.. plot::
+   :context: close-figs
+   :align: center
+
+    from sklearn.model_selection import train_test_split
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.metrics import RocCurveDisplay
+    from sklearn.datasets import load_iris
+
+    X, y = load_iris(return_X_y=True)
+    y = y == 2  # make binary
+    X_train, X_test, y_train, y_test = train_test_split(
+       X, y, test_size=.8, random_state=42
+    )
+    clf = LogisticRegression(random_state=42, C=.01)
+    clf.fit(X_train, y_train)
+
+    # select the probability of the class that we considered to be the positive label
+    y_pred = clf.predict_proba(X_test)[:, 1]
+
+    clf_disp = RocCurveDisplay.from_predictions(y_test, y_pred)
+
+
+The returned `clf_disp` object allows us to add another curve to the already computed
+ROC curve. In this case, the `clf_disp` is a :class:`~sklearn.metrics.RocCurveDisplay`
+that stores the computed values as attributes called `roc_auc`, `fpr`, and `tpr`.
+
+Next, we train a random forest classifier and plot the previously computed ROC curve
+again by using the `plot` method of the `Display` object.
 
 .. plot::
    :context: close-figs
@@ -52,11 +101,12 @@ the previously computed ROC curve again by using the `plot` method of the
 
     ax = plt.gca()
     rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
-    svc_disp.plot(ax=ax, alpha=0.8)
+    clf_disp.plot(ax=ax, alpha=0.8)
 
 Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha
 values of the curves.
 
+
 .. rubric:: Examples
 
 * :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`

From 7782c8fdf48805a29ba49141841527811a94cde7 Mon Sep 17 00:00:00 2001
From: Lucy Liu 
Date: Tue, 27 May 2025 08:45:16 +1000
Subject: [PATCH 45/64] FEA add `from_cv_results` in `RocCurveDisplay` (#30399)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Jérémie du Boisberranger 
---
 .../sklearn.metrics/30399.feature.rst         |   4 +
 sklearn/metrics/_plot/roc_curve.py            | 381 ++++++++-
 .../_plot/tests/test_common_curve_display.py  |  37 +-
 .../_plot/tests/test_roc_curve_display.py     | 746 ++++++++++++++++--
 sklearn/utils/_plotting.py                    | 244 +++++-
 sklearn/utils/tests/test_plotting.py          | 296 +++++++
 6 files changed, 1574 insertions(+), 134 deletions(-)
 create mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst

diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst
new file mode 100644
index 0000000000000..c3b6d77c5aefb
--- /dev/null
+++ b/doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst
@@ -0,0 +1,4 @@
+- Add class method `from_cv_results` to :class:`metrics.RocCurveDisplay`, which allows
+  easy plotting of multiple ROC curves from :func:`model_selection.cross_validate`
+  results.
+  By :user:`Lucy Liu `
diff --git a/sklearn/metrics/_plot/roc_curve.py b/sklearn/metrics/_plot/roc_curve.py
index b20569ea17f0b..439c6cfcdd996 100644
--- a/sklearn/metrics/_plot/roc_curve.py
+++ b/sklearn/metrics/_plot/roc_curve.py
@@ -1,13 +1,21 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+
 import warnings
 
+import numpy as np
+
+from ...utils import _safe_indexing
 from ...utils._plotting import (
     _BinaryClassifierCurveDisplayMixin,
+    _check_param_lengths,
+    _convert_to_list_leaving_none,
+    _deprecate_estimator_name,
     _despine,
     _validate_style_kwargs,
 )
+from ...utils._response import _get_response_values_binary
 from .._ranking import auc, roc_curve
 
 
@@ -16,7 +24,8 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
 
     It is recommended to use
     :func:`~sklearn.metrics.RocCurveDisplay.from_estimator` or
-    :func:`~sklearn.metrics.RocCurveDisplay.from_predictions` to create
+    :func:`~sklearn.metrics.RocCurveDisplay.from_predictions` or
+    :func:`~sklearn.metrics.RocCurveDisplay.from_cv_results` to create
     a :class:`~sklearn.metrics.RocCurveDisplay`. All parameters are
     stored as attributes.
 
@@ -27,17 +36,40 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
 
     Parameters
     ----------
-    fpr : ndarray
-        False positive rate.
+    fpr : ndarray or list of ndarrays
+        False positive rates. Each ndarray should contain values for a single curve.
+        If plotting multiple curves, list should be of same length as `tpr`.
 
-    tpr : ndarray
-        True positive rate.
+        .. versionchanged:: 1.7
+            Now accepts a list for plotting multiple curves.
 
-    roc_auc : float, default=None
-        Area under ROC curve. If None, the roc_auc score is not shown.
+    tpr : ndarray or list of ndarrays
+        True positive rates. Each ndarray should contain values for a single curve.
+        If plotting multiple curves, list should be of same length as `fpr`.
 
-    estimator_name : str, default=None
-        Name of estimator. If None, the estimator name is not shown.
+        .. versionchanged:: 1.7
+            Now accepts a list for plotting multiple curves.
+
+    roc_auc : float or list of floats, default=None
+        Area under ROC curve, used for labeling each curve in the legend.
+        If plotting multiple curves, should be a list of the same length as `fpr`
+        and `tpr`. If `None`, ROC AUC scores are not shown in the legend.
+
+        .. versionchanged:: 1.7
+            Now accepts a list for plotting multiple curves.
+
+    name : str or list of str, default=None
+        Name for labeling legend entries. The number of legend entries
+        is determined by the `curve_kwargs` passed to `plot`.
+        To label each curve, provide a list of strings. To avoid labeling
+        individual curves that have the same appearance, this cannot be used in
+        conjunction with `curve_kwargs` being a dictionary or None. If a
+        string is provided, it will be used to either label the single legend entry
+        or if there are multiple legend entries, label each individual curve with
+        the same name. If `None`, set to `name` provided at `RocCurveDisplay`
+        initialization. If still `None`, no name is shown in the legend.
+
+        .. versionadded:: 1.7
 
     pos_label : int, float, bool or str, default=None
         The class considered as the positive class when computing the roc auc
@@ -46,10 +78,21 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
 
         .. versionadded:: 0.24
 
+    estimator_name : str, default=None
+        Name of estimator. If None, the estimator name is not shown.
+
+        .. deprecated:: 1.7
+            `estimator_name` is deprecated and will be removed in 1.9. Use `name`
+            instead.
+
     Attributes
     ----------
-    line_ : matplotlib Artist
-        ROC Curve.
+    line_ : matplotlib Artist or list of matplotlib Artists
+        ROC Curves.
+
+        .. versionchanged:: 1.7
+            This attribute can now be a list of Artists, for when multiple curves
+            are plotted.
 
     chance_level_ : matplotlib Artist or None
         The chance level line. It is `None` if the chance level is not plotted.
@@ -81,24 +124,52 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
     >>> fpr, tpr, thresholds = metrics.roc_curve(y_true, y_score)
     >>> roc_auc = metrics.auc(fpr, tpr)
     >>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
-    ...                                   estimator_name='example estimator')
+    ...                                   name='example estimator')
     >>> display.plot()
     <...>
     >>> plt.show()
     """
 
-    def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=None):
-        self.estimator_name = estimator_name
+    def __init__(
+        self,
+        *,
+        fpr,
+        tpr,
+        roc_auc=None,
+        name=None,
+        pos_label=None,
+        estimator_name="deprecated",
+    ):
         self.fpr = fpr
         self.tpr = tpr
         self.roc_auc = roc_auc
+        self.name = _deprecate_estimator_name(estimator_name, name, "1.7")
         self.pos_label = pos_label
 
+    def _validate_plot_params(self, *, ax, name):
+        self.ax_, self.figure_, name = super()._validate_plot_params(ax=ax, name=name)
+
+        fpr = _convert_to_list_leaving_none(self.fpr)
+        tpr = _convert_to_list_leaving_none(self.tpr)
+        roc_auc = _convert_to_list_leaving_none(self.roc_auc)
+        name = _convert_to_list_leaving_none(name)
+
+        optional = {"self.roc_auc": roc_auc}
+        if isinstance(name, list) and len(name) != 1:
+            optional.update({"'name' (or self.name)": name})
+        _check_param_lengths(
+            required={"self.fpr": fpr, "self.tpr": tpr},
+            optional=optional,
+            class_name="RocCurveDisplay",
+        )
+        return fpr, tpr, roc_auc, name
+
     def plot(
         self,
         ax=None,
         *,
         name=None,
+        curve_kwargs=None,
         plot_chance_level=False,
         chance_level_kw=None,
         despine=False,
@@ -106,17 +177,36 @@ def plot(
     ):
         """Plot visualization.
 
-        Extra keyword arguments will be passed to matplotlib's ``plot``.
-
         Parameters
         ----------
         ax : matplotlib axes, default=None
             Axes object to plot on. If `None`, a new figure and axes is
             created.
 
-        name : str, default=None
-            Name of ROC Curve for labeling. If `None`, use `estimator_name` if
-            not `None`, otherwise no labeling is shown.
+        name : str or list of str, default=None
+            Name for labeling legend entries. The number of legend entries
+            is determined by `curve_kwargs`.
+            To label each curve, provide a list of strings. To avoid labeling
+            individual curves that have the same appearance, this cannot be used in
+            conjunction with `curve_kwargs` being a dictionary or None. If a
+            string is provided, it will be used to either label the single legend entry
+            or if there are multiple legend entries, label each individual curve with
+            the same name. If `None`, set to `name` provided at `RocCurveDisplay`
+            initialization. If still `None`, no name is shown in the legend.
+
+            .. versionadded:: 1.7
+
+        curve_kwargs : dict or list of dict, default=None
+            Keywords arguments to be passed to matplotlib's `plot` function
+            to draw individual ROC curves. For single curve plotting, should be
+            a dictionary. For multi-curve plotting, if a list is provided the
+            parameters are applied to the ROC curves of each CV fold
+            sequentially and a legend entry is added for each curve.
+            If a single dictionary is provided, the same parameters are applied
+            to all ROC curves and a single legend entry for all curves is added,
+            labeled with the mean ROC AUC score.
+
+            .. versionadded:: 1.7
 
         plot_chance_level : bool, default=False
             Whether to plot the chance level.
@@ -137,22 +227,34 @@ def plot(
         **kwargs : dict
             Keyword arguments to be passed to matplotlib's `plot`.
 
+            .. deprecated:: 1.7
+                kwargs is deprecated and will be removed in 1.9. Pass matplotlib
+                arguments to `curve_kwargs` as a dictionary instead.
+
         Returns
         -------
         display : :class:`~sklearn.metrics.RocCurveDisplay`
             Object that stores computed values.
         """
-        self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name)
-
-        default_line_kwargs = {}
-        if self.roc_auc is not None and name is not None:
-            default_line_kwargs["label"] = f"{name} (AUC = {self.roc_auc:0.2f})"
-        elif self.roc_auc is not None:
-            default_line_kwargs["label"] = f"AUC = {self.roc_auc:0.2f}"
-        elif name is not None:
-            default_line_kwargs["label"] = name
-
-        line_kwargs = _validate_style_kwargs(default_line_kwargs, kwargs)
+        fpr, tpr, roc_auc, name = self._validate_plot_params(ax=ax, name=name)
+        n_curves = len(fpr)
+        if not isinstance(curve_kwargs, list) and n_curves > 1:
+            if roc_auc:
+                legend_metric = {"mean": np.mean(roc_auc), "std": np.std(roc_auc)}
+            else:
+                legend_metric = {"mean": None, "std": None}
+        else:
+            roc_auc = roc_auc if roc_auc is not None else [None] * n_curves
+            legend_metric = {"metric": roc_auc}
+
+        curve_kwargs = self._validate_curve_kwargs(
+            n_curves,
+            name,
+            legend_metric,
+            "AUC",
+            curve_kwargs=curve_kwargs,
+            **kwargs,
+        )
 
         default_chance_level_line_kw = {
             "label": "Chance level (AUC = 0.5)",
@@ -167,7 +269,13 @@ def plot(
             default_chance_level_line_kw, chance_level_kw
         )
 
-        (self.line_,) = self.ax_.plot(self.fpr, self.tpr, **line_kwargs)
+        self.line_ = []
+        for fpr, tpr, line_kw in zip(fpr, tpr, curve_kwargs):
+            self.line_.extend(self.ax_.plot(fpr, tpr, **line_kw))
+        # Return single artist if only one curve is plotted
+        if len(self.line_) == 1:
+            self.line_ = self.line_[0]
+
         info_pos_label = (
             f" (Positive label: {self.pos_label})" if self.pos_label is not None else ""
         )
@@ -190,9 +298,8 @@ def plot(
         if despine:
             _despine(self.ax_)
 
-        if (
-            line_kwargs.get("label") is not None
-            or chance_level_kw.get("label") is not None
+        if curve_kwargs[0].get("label") is not None or (
+            plot_chance_level and chance_level_kw.get("label") is not None
         ):
             self.ax_.legend(loc="lower right")
 
@@ -211,6 +318,7 @@ def from_estimator(
         pos_label=None,
         name=None,
         ax=None,
+        curve_kwargs=None,
         plot_chance_level=False,
         chance_level_kw=None,
         despine=False,
@@ -252,8 +360,8 @@ def from_estimator(
             :term:`decision_function` is tried next.
 
         pos_label : int, float, bool or str, default=None
-            The class considered as the positive class when computing the roc auc
-            metrics. By default, `estimators.classes_[1]` is considered
+            The class considered as the positive class when computing the ROC AUC.
+            By default, `estimators.classes_[1]` is considered
             as the positive class.
 
         name : str, default=None
@@ -263,6 +371,11 @@ def from_estimator(
         ax : matplotlib axes, default=None
             Axes object to plot on. If `None`, a new figure and axes is created.
 
+        curve_kwargs : dict, default=None
+            Keywords arguments to be passed to matplotlib's `plot` function.
+
+            .. versionadded:: 1.7
+
         plot_chance_level : bool, default=False
             Whether to plot the chance level.
 
@@ -282,6 +395,10 @@ def from_estimator(
         **kwargs : dict
             Keyword arguments to be passed to matplotlib's `plot`.
 
+            .. deprecated:: 1.7
+                kwargs is deprecated and will be removed in 1.9. Pass matplotlib
+                arguments to `curve_kwargs` as a dictionary instead.
+
         Returns
         -------
         display : :class:`~sklearn.metrics.RocCurveDisplay`
@@ -327,6 +444,7 @@ def from_estimator(
             name=name,
             ax=ax,
             pos_label=pos_label,
+            curve_kwargs=curve_kwargs,
             plot_chance_level=plot_chance_level,
             chance_level_kw=chance_level_kw,
             despine=despine,
@@ -344,6 +462,7 @@ def from_predictions(
         pos_label=None,
         name=None,
         ax=None,
+        curve_kwargs=None,
         plot_chance_level=False,
         chance_level_kw=None,
         despine=False,
@@ -382,18 +501,23 @@ def from_predictions(
             points.
 
         pos_label : int, float, bool or str, default=None
-            The label of the positive class. When `pos_label=None`, if `y_true`
-            is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
-            error will be raised.
+            The label of the positive class when computing the ROC AUC.
+            When `pos_label=None`, if `y_true` is in {-1, 1} or {0, 1}, `pos_label`
+            is set to 1, otherwise an error will be raised.
 
         name : str, default=None
-            Name of ROC curve for labeling. If `None`, name will be set to
+            Name of ROC curve for legend labeling. If `None`, name will be set to
             `"Classifier"`.
 
         ax : matplotlib axes, default=None
             Axes object to plot on. If `None`, a new figure and axes is
             created.
 
+        curve_kwargs : dict, default=None
+            Keywords arguments to be passed to matplotlib's `plot` function.
+
+            .. versionadded:: 1.7
+
         plot_chance_level : bool, default=False
             Whether to plot the chance level.
 
@@ -422,6 +546,10 @@ def from_predictions(
         **kwargs : dict
             Additional keywords arguments passed to matplotlib `plot` function.
 
+            .. deprecated:: 1.7
+                kwargs is deprecated and will be removed in 1.9. Pass matplotlib
+                arguments to `curve_kwargs` as a dictionary instead.
+
         Returns
         -------
         display : :class:`~sklearn.metrics.RocCurveDisplay`
@@ -485,15 +613,184 @@ def from_predictions(
             fpr=fpr,
             tpr=tpr,
             roc_auc=roc_auc,
-            estimator_name=name,
+            name=name,
             pos_label=pos_label_validated,
         )
 
         return viz.plot(
             ax=ax,
-            name=name,
+            curve_kwargs=curve_kwargs,
             plot_chance_level=plot_chance_level,
             chance_level_kw=chance_level_kw,
             despine=despine,
             **kwargs,
         )
+
+    @classmethod
+    def from_cv_results(
+        cls,
+        cv_results,
+        X,
+        y,
+        *,
+        sample_weight=None,
+        drop_intermediate=True,
+        response_method="auto",
+        pos_label=None,
+        ax=None,
+        name=None,
+        curve_kwargs=None,
+        plot_chance_level=False,
+        chance_level_kwargs=None,
+        despine=False,
+    ):
+        """Create a multi-fold ROC curve display given cross-validation results.
+
+        .. versionadded:: 1.7
+
+        Parameters
+        ----------
+        cv_results : dict
+            Dictionary as returned by :func:`~sklearn.model_selection.cross_validate`
+            using `return_estimator=True` and `return_indices=True` (i.e., dictionary
+            should contain the keys "estimator" and "indices").
+
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Input values.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        drop_intermediate : bool, default=True
+            Whether to drop some suboptimal thresholds which would not appear
+            on a plotted ROC curve. This is useful in order to create lighter
+            ROC curves.
+
+        response_method : {'predict_proba', 'decision_function', 'auto'} \
+                default='auto'
+            Specifies whether to use :term:`predict_proba` or
+            :term:`decision_function` as the target response. If set to 'auto',
+            :term:`predict_proba` is tried first and if it does not exist
+            :term:`decision_function` is tried next.
+
+        pos_label : int, float, bool or str, default=None
+            The class considered as the positive class when computing the ROC AUC
+            metrics. By default, `estimators.classes_[1]` is considered
+            as the positive class.
+
+        ax : matplotlib axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        name : str or list of str, default=None
+            Name for labeling legend entries. The number of legend entries
+            is determined by `curve_kwargs`.
+            To label each curve, provide a list of strings. To avoid labeling
+            individual curves that have the same appearance, this cannot be used in
+            conjunction with `curve_kwargs` being a dictionary or None. If a
+            string is provided, it will be used to either label the single legend entry
+            or if there are multiple legend entries, label each individual curve with
+            the same name. If `None`, no name is shown in the legend.
+
+        curve_kwargs : dict or list of dict, default=None
+            Keywords arguments to be passed to matplotlib's `plot` function
+            to draw individual ROC curves. If a list is provided the
+            parameters are applied to the ROC curves of each CV fold
+            sequentially and a legend entry is added for each curve.
+            If a single dictionary is provided, the same parameters are applied
+            to all ROC curves and a single legend entry for all curves is added,
+            labeled with the mean ROC AUC score.
+
+        plot_chance_level : bool, default=False
+            Whether to plot the chance level.
+
+        chance_level_kwargs : dict, default=None
+            Keyword arguments to be passed to matplotlib's `plot` for rendering
+            the chance level line.
+
+        despine : bool, default=False
+            Whether to remove the top and right spines from the plot.
+
+        Returns
+        -------
+        display : :class:`~sklearn.metrics.RocCurveDisplay`
+            The multi-fold ROC curve display.
+
+        See Also
+        --------
+        roc_curve : Compute Receiver operating characteristic (ROC) curve.
+            RocCurveDisplay.from_estimator : ROC Curve visualization given an
+            estimator and some data.
+        RocCurveDisplay.from_predictions : ROC Curve visualization given the
+            probabilities of scores of a classifier.
+        roc_auc_score : Compute the area under the ROC curve.
+
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.metrics import RocCurveDisplay
+        >>> from sklearn.model_selection import cross_validate
+        >>> from sklearn.svm import SVC
+        >>> X, y = make_classification(random_state=0)
+        >>> clf = SVC(random_state=0)
+        >>> cv_results = cross_validate(
+        ...     clf, X, y, cv=3, return_estimator=True, return_indices=True)
+        >>> RocCurveDisplay.from_cv_results(cv_results, X, y)
+        <...>
+        >>> plt.show()
+        """
+        pos_label_ = cls._validate_from_cv_results_params(
+            cv_results,
+            X,
+            y,
+            sample_weight=sample_weight,
+            pos_label=pos_label,
+        )
+
+        fpr_folds, tpr_folds, auc_folds = [], [], []
+        for estimator, test_indices in zip(
+            cv_results["estimator"], cv_results["indices"]["test"]
+        ):
+            y_true = _safe_indexing(y, test_indices)
+            y_pred, _ = _get_response_values_binary(
+                estimator,
+                _safe_indexing(X, test_indices),
+                response_method=response_method,
+                pos_label=pos_label_,
+            )
+            sample_weight_fold = (
+                None
+                if sample_weight is None
+                else _safe_indexing(sample_weight, test_indices)
+            )
+            fpr, tpr, _ = roc_curve(
+                y_true,
+                y_pred,
+                pos_label=pos_label_,
+                sample_weight=sample_weight_fold,
+                drop_intermediate=drop_intermediate,
+            )
+            roc_auc = auc(fpr, tpr)
+
+            fpr_folds.append(fpr)
+            tpr_folds.append(tpr)
+            auc_folds.append(roc_auc)
+
+        viz = cls(
+            fpr=fpr_folds,
+            tpr=tpr_folds,
+            name=name,
+            roc_auc=auc_folds,
+            pos_label=pos_label_,
+        )
+        return viz.plot(
+            ax=ax,
+            curve_kwargs=curve_kwargs,
+            plot_chance_level=plot_chance_level,
+            chance_level_kw=chance_level_kwargs,
+            despine=despine,
+        )
diff --git a/sklearn/metrics/_plot/tests/test_common_curve_display.py b/sklearn/metrics/_plot/tests/test_common_curve_display.py
index 0014a73055e41..753f2a1e7319d 100644
--- a/sklearn/metrics/_plot/tests/test_common_curve_display.py
+++ b/sklearn/metrics/_plot/tests/test_common_curve_display.py
@@ -132,9 +132,7 @@ def fit(self, X, y):
         Display.from_estimator(clf, X, y, response_method=response_method)
 
 
-@pytest.mark.parametrize(
-    "Display", [DetCurveDisplay, PrecisionRecallDisplay, RocCurveDisplay]
-)
+@pytest.mark.parametrize("Display", [DetCurveDisplay, PrecisionRecallDisplay])
 @pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
 def test_display_curve_estimator_name_multiple_calls(
     pyplot,
@@ -166,6 +164,8 @@ def test_display_curve_estimator_name_multiple_calls(
     assert clf_name in disp.line_.get_label()
 
 
+# TODO: remove this test once classes moved to using `name` instead of
+# `estimator_name`
 @pytest.mark.parametrize(
     "clf",
     [
@@ -176,10 +176,8 @@ def test_display_curve_estimator_name_multiple_calls(
         ),
     ],
 )
-@pytest.mark.parametrize(
-    "Display", [DetCurveDisplay, PrecisionRecallDisplay, RocCurveDisplay]
-)
-def test_display_curve_not_fitted_errors(pyplot, data_binary, clf, Display):
+@pytest.mark.parametrize("Display", [DetCurveDisplay, PrecisionRecallDisplay])
+def test_display_curve_not_fitted_errors_old_name(pyplot, data_binary, clf, Display):
     """Check that a proper error is raised when the classifier is not
     fitted."""
     X, y = data_binary
@@ -194,6 +192,31 @@ def test_display_curve_not_fitted_errors(pyplot, data_binary, clf, Display):
     assert disp.estimator_name == model.__class__.__name__
 
 
+@pytest.mark.parametrize(
+    "clf",
+    [
+        LogisticRegression(),
+        make_pipeline(StandardScaler(), LogisticRegression()),
+        make_pipeline(
+            make_column_transformer((StandardScaler(), [0, 1])), LogisticRegression()
+        ),
+    ],
+)
+@pytest.mark.parametrize("Display", [RocCurveDisplay])
+def test_display_curve_not_fitted_errors(pyplot, data_binary, clf, Display):
+    """Check that a proper error is raised when the classifier is not fitted."""
+    X, y = data_binary
+    # clone since we parametrize the test and the classifier will be fitted
+    # when testing the second and subsequent plotting function
+    model = clone(clf)
+    with pytest.raises(NotFittedError):
+        Display.from_estimator(model, X, y)
+    model.fit(X, y)
+    disp = Display.from_estimator(model, X, y)
+    assert model.__class__.__name__ in disp.line_.get_label()
+    assert disp.name == model.__class__.__name__
+
+
 @pytest.mark.parametrize(
     "Display", [DetCurveDisplay, PrecisionRecallDisplay, RocCurveDisplay]
 )
diff --git a/sklearn/metrics/_plot/tests/test_roc_curve_display.py b/sklearn/metrics/_plot/tests/test_roc_curve_display.py
index ca0d7155e7c2c..23fa2f2e3a5e6 100644
--- a/sklearn/metrics/_plot/tests/test_roc_curve_display.py
+++ b/sklearn/metrics/_plot/tests/test_roc_curve_display.py
@@ -1,3 +1,5 @@
+from collections.abc import Mapping
+
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose
@@ -9,10 +11,11 @@
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import RocCurveDisplay, auc, roc_curve
-from sklearn.model_selection import train_test_split
+from sklearn.model_selection import cross_validate, train_test_split
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
-from sklearn.utils import shuffle
+from sklearn.utils import _safe_indexing, shuffle
+from sklearn.utils._response import _get_response_values_binary
 
 
 @pytest.fixture(scope="module")
@@ -29,6 +32,24 @@ def data_binary():
     return X, y
 
 
+def _check_figure_axes_and_labels(display, pos_label):
+    """Check mpl axes and figure defaults are correct."""
+    import matplotlib as mpl
+
+    assert isinstance(display.ax_, mpl.axes.Axes)
+    assert isinstance(display.figure_, mpl.figure.Figure)
+    assert display.ax_.get_adjustable() == "box"
+    assert display.ax_.get_aspect() in ("equal", 1.0)
+    assert display.ax_.get_xlim() == display.ax_.get_ylim() == (-0.01, 1.01)
+
+    expected_pos_label = 1 if pos_label is None else pos_label
+    expected_ylabel = f"True Positive Rate (Positive label: {expected_pos_label})"
+    expected_xlabel = f"False Positive Rate (Positive label: {expected_pos_label})"
+
+    assert display.ax_.get_ylabel() == expected_ylabel
+    assert display.ax_.get_xlabel() == expected_xlabel
+
+
 @pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"])
 @pytest.mark.parametrize("with_sample_weight", [True, False])
 @pytest.mark.parametrize("drop_intermediate", [True, False])
@@ -50,7 +71,7 @@ def test_roc_curve_display_plotting(
     constructor_name,
     default_name,
 ):
-    """Check the overall plotting behaviour."""
+    """Check the overall plotting behaviour for single curve."""
     X, y = data_binary
 
     pos_label = None
@@ -78,7 +99,7 @@ def test_roc_curve_display_plotting(
             sample_weight=sample_weight,
             drop_intermediate=drop_intermediate,
             pos_label=pos_label,
-            alpha=0.8,
+            curve_kwargs={"alpha": 0.8},
         )
     else:
         display = RocCurveDisplay.from_predictions(
@@ -87,7 +108,7 @@ def test_roc_curve_display_plotting(
             sample_weight=sample_weight,
             drop_intermediate=drop_intermediate,
             pos_label=pos_label,
-            alpha=0.8,
+            curve_kwargs={"alpha": 0.8},
         )
 
     fpr, tpr, _ = roc_curve(
@@ -102,27 +123,509 @@ def test_roc_curve_display_plotting(
     assert_allclose(display.fpr, fpr)
     assert_allclose(display.tpr, tpr)
 
-    assert display.estimator_name == default_name
+    assert display.name == default_name
 
     import matplotlib as mpl
 
+    _check_figure_axes_and_labels(display, pos_label)
     assert isinstance(display.line_, mpl.lines.Line2D)
     assert display.line_.get_alpha() == 0.8
-    assert isinstance(display.ax_, mpl.axes.Axes)
-    assert isinstance(display.figure_, mpl.figure.Figure)
-    assert display.ax_.get_adjustable() == "box"
-    assert display.ax_.get_aspect() in ("equal", 1.0)
-    assert display.ax_.get_xlim() == display.ax_.get_ylim() == (-0.01, 1.01)
 
     expected_label = f"{default_name} (AUC = {display.roc_auc:.2f})"
     assert display.line_.get_label() == expected_label
 
-    expected_pos_label = 1 if pos_label is None else pos_label
-    expected_ylabel = f"True Positive Rate (Positive label: {expected_pos_label})"
-    expected_xlabel = f"False Positive Rate (Positive label: {expected_pos_label})"
 
-    assert display.ax_.get_ylabel() == expected_ylabel
-    assert display.ax_.get_xlabel() == expected_xlabel
+@pytest.mark.parametrize(
+    "params, err_msg",
+    [
+        (
+            {
+                "fpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "tpr": [np.array([0, 0.5, 1])],
+                "roc_auc": None,
+                "name": None,
+            },
+            "self.fpr and self.tpr from `RocCurveDisplay` initialization,",
+        ),
+        (
+            {
+                "fpr": [np.array([0, 0.5, 1])],
+                "tpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "roc_auc": [0.8, 0.9],
+                "name": None,
+            },
+            "self.fpr, self.tpr and self.roc_auc from `RocCurveDisplay`",
+        ),
+        (
+            {
+                "fpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "tpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "roc_auc": [0.8],
+                "name": None,
+            },
+            "Got: self.fpr: 2, self.tpr: 2, self.roc_auc: 1",
+        ),
+        (
+            {
+                "fpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "tpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "roc_auc": [0.8, 0.9],
+                "name": ["curve1", "curve2", "curve3"],
+            },
+            r"self.fpr, self.tpr, self.roc_auc and 'name' \(or self.name\)",
+        ),
+        (
+            {
+                "fpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "tpr": [np.array([0, 0.5, 1]), np.array([0, 0.5, 1])],
+                "roc_auc": [0.8, 0.9],
+                # List of length 1 is always allowed
+                "name": ["curve1"],
+            },
+            None,
+        ),
+    ],
+)
+def test_roc_curve_plot_parameter_length_validation(pyplot, params, err_msg):
+    """Check `plot` parameter length validation performed correctly."""
+    display = RocCurveDisplay(**params)
+    if err_msg:
+        with pytest.raises(ValueError, match=err_msg):
+            display.plot()
+    else:
+        # No error should be raised
+        display.plot()
+
+
+def test_validate_plot_params(pyplot):
+    """Check `_validate_plot_params` returns the correct variables."""
+    fpr = np.array([0, 0.5, 1])
+    tpr = [np.array([0, 0.5, 1])]
+    roc_auc = None
+    name = "test_curve"
+
+    # Initialize display with test inputs
+    display = RocCurveDisplay(
+        fpr=fpr,
+        tpr=tpr,
+        roc_auc=roc_auc,
+        name=name,
+        pos_label=None,
+    )
+    fpr_out, tpr_out, roc_auc_out, name_out = display._validate_plot_params(
+        ax=None, name=None
+    )
+
+    assert isinstance(fpr_out, list)
+    assert isinstance(tpr_out, list)
+    assert len(fpr_out) == 1
+    assert len(tpr_out) == 1
+    assert roc_auc_out is None
+    assert name_out == ["test_curve"]
+
+
+def test_roc_curve_from_cv_results_param_validation(pyplot, data_binary):
+    """Check parameter validation is correct."""
+    X, y = data_binary
+
+    # `cv_results` missing key
+    cv_results_no_est = cross_validate(
+        LogisticRegression(), X, y, cv=3, return_estimator=True, return_indices=False
+    )
+    cv_results_no_indices = cross_validate(
+        LogisticRegression(), X, y, cv=3, return_estimator=True, return_indices=False
+    )
+    for cv_results in (cv_results_no_est, cv_results_no_indices):
+        with pytest.raises(
+            ValueError,
+            match="`cv_results` does not contain one of the following required",
+        ):
+            RocCurveDisplay.from_cv_results(cv_results, X, y)
+
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=3, return_estimator=True, return_indices=True
+    )
+
+    # `X` wrong length
+    with pytest.raises(ValueError, match="`X` does not contain the correct"):
+        RocCurveDisplay.from_cv_results(cv_results, X[:10, :], y)
+
+    # `y` not binary
+    y_multi = y.copy()
+    y_multi[0] = 2
+    with pytest.raises(ValueError, match="The target `y` is not binary."):
+        RocCurveDisplay.from_cv_results(cv_results, X, y_multi)
+
+    # input inconsistent length
+    with pytest.raises(ValueError, match="Found input variables with inconsistent"):
+        RocCurveDisplay.from_cv_results(cv_results, X, y[:10])
+    with pytest.raises(ValueError, match="Found input variables with inconsistent"):
+        RocCurveDisplay.from_cv_results(cv_results, X, y, sample_weight=[1, 2])
+
+    # `pos_label` inconsistency
+    y_multi[y_multi == 1] = 2
+    with pytest.raises(ValueError, match=r"y takes value in \{0, 2\}"):
+        RocCurveDisplay.from_cv_results(cv_results, X, y_multi)
+
+    # `name` is list while `curve_kwargs` is None or dict
+    for curve_kwargs in (None, {"alpha": 0.2}):
+        with pytest.raises(ValueError, match="To avoid labeling individual curves"):
+            RocCurveDisplay.from_cv_results(
+                cv_results,
+                X,
+                y,
+                name=["one", "two", "three"],
+                curve_kwargs=curve_kwargs,
+            )
+
+    # `curve_kwargs` incorrect length
+    with pytest.raises(ValueError, match="`curve_kwargs` must be None, a dictionary"):
+        RocCurveDisplay.from_cv_results(cv_results, X, y, curve_kwargs=[{"alpha": 1}])
+
+    # `curve_kwargs` both alias provided
+    with pytest.raises(TypeError, match="Got both c and"):
+        RocCurveDisplay.from_cv_results(
+            cv_results, X, y, curve_kwargs={"c": "blue", "color": "red"}
+        )
+
+
+@pytest.mark.parametrize(
+    "curve_kwargs",
+    [None, {"alpha": 0.2}, [{"alpha": 0.2}, {"alpha": 0.3}, {"alpha": 0.4}]],
+)
+def test_roc_curve_display_from_cv_results_curve_kwargs(
+    pyplot, data_binary, curve_kwargs
+):
+    """Check `curve_kwargs` correctly passed."""
+    X, y = data_binary
+    n_cv = 3
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=n_cv, return_estimator=True, return_indices=True
+    )
+    display = RocCurveDisplay.from_cv_results(
+        cv_results,
+        X,
+        y,
+        curve_kwargs=curve_kwargs,
+    )
+    if curve_kwargs is None:
+        # Default `alpha` used
+        assert all(line.get_alpha() == 0.5 for line in display.line_)
+    elif isinstance(curve_kwargs, Mapping):
+        # `alpha` from dict used for all curves
+        assert all(line.get_alpha() == 0.2 for line in display.line_)
+    else:
+        # Different `alpha` used for each curve
+        assert all(
+            line.get_alpha() == curve_kwargs[i]["alpha"]
+            for i, line in enumerate(display.line_)
+        )
+
+
+# TODO(1.9): Remove in 1.9
+def test_roc_curve_display_estimator_name_deprecation(pyplot):
+    """Check deprecation of `estimator_name`."""
+    fpr = np.array([0, 0.5, 1])
+    tpr = np.array([0, 0.5, 1])
+    with pytest.warns(FutureWarning, match="`estimator_name` is deprecated in"):
+        RocCurveDisplay(fpr=fpr, tpr=tpr, estimator_name="test")
+
+
+# TODO(1.9): Remove in 1.9
+@pytest.mark.parametrize(
+    "constructor_name", ["from_estimator", "from_predictions", "plot"]
+)
+def test_roc_curve_display_kwargs_deprecation(pyplot, data_binary, constructor_name):
+    """Check **kwargs deprecated correctly in favour of `curve_kwargs`."""
+    X, y = data_binary
+    lr = LogisticRegression()
+    lr.fit(X, y)
+    fpr = np.array([0, 0.5, 1])
+    tpr = np.array([0, 0.5, 1])
+
+    # Error when both `curve_kwargs` and `**kwargs` provided
+    with pytest.raises(ValueError, match="Cannot provide both `curve_kwargs`"):
+        if constructor_name == "from_estimator":
+            RocCurveDisplay.from_estimator(
+                lr, X, y, curve_kwargs={"alpha": 1}, label="test"
+            )
+        elif constructor_name == "from_predictions":
+            RocCurveDisplay.from_predictions(
+                y, y, curve_kwargs={"alpha": 1}, label="test"
+            )
+        else:
+            RocCurveDisplay(fpr=fpr, tpr=tpr).plot(
+                curve_kwargs={"alpha": 1}, label="test"
+            )
+
+    # Warning when `**kwargs`` provided
+    with pytest.warns(FutureWarning, match=r"`\*\*kwargs` is deprecated and will be"):
+        if constructor_name == "from_estimator":
+            RocCurveDisplay.from_estimator(lr, X, y, label="test")
+        elif constructor_name == "from_predictions":
+            RocCurveDisplay.from_predictions(y, y, label="test")
+        else:
+            RocCurveDisplay(fpr=fpr, tpr=tpr).plot(label="test")
+
+
+@pytest.mark.parametrize(
+    "curve_kwargs",
+    [
+        None,
+        {"color": "blue"},
+        [{"color": "blue"}, {"color": "green"}, {"color": "red"}],
+    ],
+)
+@pytest.mark.parametrize("drop_intermediate", [True, False])
+@pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"])
+@pytest.mark.parametrize("with_sample_weight", [True, False])
+@pytest.mark.parametrize("with_strings", [True, False])
+def test_roc_curve_display_plotting_from_cv_results(
+    pyplot,
+    data_binary,
+    with_strings,
+    with_sample_weight,
+    response_method,
+    drop_intermediate,
+    curve_kwargs,
+):
+    """Check overall plotting of `from_cv_results`."""
+    X, y = data_binary
+
+    pos_label = None
+    if with_strings:
+        y = np.array(["c", "b"])[y]
+        pos_label = "c"
+
+    if with_sample_weight:
+        rng = np.random.RandomState(42)
+        sample_weight = rng.randint(1, 4, size=(X.shape[0]))
+    else:
+        sample_weight = None
+
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=3, return_estimator=True, return_indices=True
+    )
+    display = RocCurveDisplay.from_cv_results(
+        cv_results,
+        X,
+        y,
+        sample_weight=sample_weight,
+        drop_intermediate=drop_intermediate,
+        response_method=response_method,
+        pos_label=pos_label,
+        curve_kwargs=curve_kwargs,
+    )
+
+    for idx, (estimator, test_indices) in enumerate(
+        zip(cv_results["estimator"], cv_results["indices"]["test"])
+    ):
+        y_true = _safe_indexing(y, test_indices)
+        y_pred = _get_response_values_binary(
+            estimator,
+            _safe_indexing(X, test_indices),
+            response_method=response_method,
+            pos_label=pos_label,
+        )[0]
+        sample_weight_fold = (
+            None
+            if sample_weight is None
+            else _safe_indexing(sample_weight, test_indices)
+        )
+        fpr, tpr, _ = roc_curve(
+            y_true,
+            y_pred,
+            sample_weight=sample_weight_fold,
+            drop_intermediate=drop_intermediate,
+            pos_label=pos_label,
+        )
+        assert_allclose(display.roc_auc[idx], auc(fpr, tpr))
+        assert_allclose(display.fpr[idx], fpr)
+        assert_allclose(display.tpr[idx], tpr)
+
+    assert display.name is None
+
+    import matplotlib as mpl
+
+    _check_figure_axes_and_labels(display, pos_label)
+    if with_sample_weight:
+        aggregate_expected_labels = ["AUC = 0.64 +/- 0.04", "_child1", "_child2"]
+    else:
+        aggregate_expected_labels = ["AUC = 0.61 +/- 0.05", "_child1", "_child2"]
+    for idx, line in enumerate(display.line_):
+        assert isinstance(line, mpl.lines.Line2D)
+        # Default alpha for `from_cv_results`
+        line.get_alpha() == 0.5
+        if isinstance(curve_kwargs, list):
+            # Each individual curve labelled
+            assert line.get_label() == f"AUC = {display.roc_auc[idx]:.2f}"
+        else:
+            # Single aggregate label
+            assert line.get_label() == aggregate_expected_labels[idx]
+
+
+@pytest.mark.parametrize("roc_auc", [[1.0, 1.0, 1.0], None])
+@pytest.mark.parametrize(
+    "curve_kwargs",
+    [None, {"color": "red"}, [{"c": "red"}, {"c": "green"}, {"c": "yellow"}]],
+)
+@pytest.mark.parametrize("name", [None, "single", ["one", "two", "three"]])
+def test_roc_curve_plot_legend_label(pyplot, data_binary, name, curve_kwargs, roc_auc):
+    """Check legend label correct with all `curve_kwargs`, `name` combinations."""
+    fpr = [np.array([0, 0.5, 1]), np.array([0, 0.5, 1]), np.array([0, 0.5, 1])]
+    tpr = [np.array([0, 0.5, 1]), np.array([0, 0.5, 1]), np.array([0, 0.5, 1])]
+    if not isinstance(curve_kwargs, list) and isinstance(name, list):
+        with pytest.raises(ValueError, match="To avoid labeling individual curves"):
+            RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc).plot(
+                name=name, curve_kwargs=curve_kwargs
+            )
+
+    else:
+        display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc).plot(
+            name=name, curve_kwargs=curve_kwargs
+        )
+        legend = display.ax_.get_legend()
+        if legend is None:
+            # No legend is created, exit test early
+            assert name is None
+            assert roc_auc is None
+            return
+        else:
+            legend_labels = [text.get_text() for text in legend.get_texts()]
+
+        if isinstance(curve_kwargs, list):
+            # Multiple labels in legend
+            assert len(legend_labels) == 3
+            for idx, label in enumerate(legend_labels):
+                if name is None:
+                    expected_label = "AUC = 1.00" if roc_auc else None
+                    assert label == expected_label
+                elif isinstance(name, str):
+                    expected_label = "single (AUC = 1.00)" if roc_auc else "single"
+                    assert label == expected_label
+                else:
+                    # `name` is a list of different strings
+                    expected_label = (
+                        f"{name[idx]} (AUC = 1.00)" if roc_auc else f"{name[idx]}"
+                    )
+                    assert label == expected_label
+        else:
+            # Single label in legend
+            assert len(legend_labels) == 1
+            if name is None:
+                expected_label = "AUC = 1.00 +/- 0.00" if roc_auc else None
+                assert legend_labels[0] == expected_label
+            else:
+                # name is single string
+                expected_label = "single (AUC = 1.00 +/- 0.00)" if roc_auc else "single"
+                assert legend_labels[0] == expected_label
+
+
+@pytest.mark.parametrize(
+    "curve_kwargs",
+    [None, {"color": "red"}, [{"c": "red"}, {"c": "green"}, {"c": "yellow"}]],
+)
+@pytest.mark.parametrize("name", [None, "single", ["one", "two", "three"]])
+def test_roc_curve_from_cv_results_legend_label(
+    pyplot, data_binary, name, curve_kwargs
+):
+    """Check legend label correct with all `curve_kwargs`, `name` combinations."""
+    X, y = data_binary
+    n_cv = 3
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=n_cv, return_estimator=True, return_indices=True
+    )
+
+    if not isinstance(curve_kwargs, list) and isinstance(name, list):
+        with pytest.raises(ValueError, match="To avoid labeling individual curves"):
+            RocCurveDisplay.from_cv_results(
+                cv_results, X, y, name=name, curve_kwargs=curve_kwargs
+            )
+    else:
+        display = RocCurveDisplay.from_cv_results(
+            cv_results, X, y, name=name, curve_kwargs=curve_kwargs
+        )
+
+        legend = display.ax_.get_legend()
+        legend_labels = [text.get_text() for text in legend.get_texts()]
+        if isinstance(curve_kwargs, list):
+            # Multiple labels in legend
+            assert len(legend_labels) == 3
+            auc = ["0.62", "0.66", "0.55"]
+            for idx, label in enumerate(legend_labels):
+                if name is None:
+                    assert label == f"AUC = {auc[idx]}"
+                elif isinstance(name, str):
+                    assert label == f"single (AUC = {auc[idx]})"
+                else:
+                    # `name` is a list of different strings
+                    assert label == f"{name[idx]} (AUC = {auc[idx]})"
+        else:
+            # Single label in legend
+            assert len(legend_labels) == 1
+            if name is None:
+                assert legend_labels[0] == "AUC = 0.61 +/- 0.05"
+            else:
+                # name is single string
+                assert legend_labels[0] == "single (AUC = 0.61 +/- 0.05)"
+
+
+@pytest.mark.parametrize(
+    "curve_kwargs",
+    [None, {"color": "red"}, [{"c": "red"}, {"c": "green"}, {"c": "yellow"}]],
+)
+def test_roc_curve_from_cv_results_curve_kwargs(pyplot, data_binary, curve_kwargs):
+    """Check line kwargs passed correctly in `from_cv_results`."""
+
+    X, y = data_binary
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=3, return_estimator=True, return_indices=True
+    )
+    display = RocCurveDisplay.from_cv_results(
+        cv_results, X, y, curve_kwargs=curve_kwargs
+    )
+
+    for idx, line in enumerate(display.line_):
+        color = line.get_color()
+        if curve_kwargs is None:
+            # Default color
+            assert color == "blue"
+        elif isinstance(curve_kwargs, Mapping):
+            # All curves "red"
+            assert color == "red"
+        else:
+            assert color == curve_kwargs[idx]["c"]
+
+
+def _check_chance_level(plot_chance_level, chance_level_kw, display):
+    """Check chance level line and line styles correct."""
+    import matplotlib as mpl
+
+    if plot_chance_level:
+        assert isinstance(display.chance_level_, mpl.lines.Line2D)
+        assert tuple(display.chance_level_.get_xdata()) == (0, 1)
+        assert tuple(display.chance_level_.get_ydata()) == (0, 1)
+    else:
+        assert display.chance_level_ is None
+
+    # Checking for chance level line styles
+    if plot_chance_level and chance_level_kw is None:
+        assert display.chance_level_.get_color() == "k"
+        assert display.chance_level_.get_linestyle() == "--"
+        assert display.chance_level_.get_label() == "Chance level (AUC = 0.5)"
+    elif plot_chance_level:
+        if "c" in chance_level_kw:
+            assert display.chance_level_.get_color() == chance_level_kw["c"]
+        else:
+            assert display.chance_level_.get_color() == chance_level_kw["color"]
+        if "lw" in chance_level_kw:
+            assert display.chance_level_.get_linewidth() == chance_level_kw["lw"]
+        else:
+            assert display.chance_level_.get_linewidth() == chance_level_kw["linewidth"]
+        if "ls" in chance_level_kw:
+            assert display.chance_level_.get_linestyle() == chance_level_kw["ls"]
+        else:
+            assert display.chance_level_.get_linestyle() == chance_level_kw["linestyle"]
 
 
 @pytest.mark.parametrize("plot_chance_level", [True, False])
@@ -136,10 +639,7 @@ def test_roc_curve_display_plotting(
         {"lw": 1, "color": "blue", "ls": "-", "label": None},
     ],
 )
-@pytest.mark.parametrize(
-    "constructor_name",
-    ["from_estimator", "from_predictions"],
-)
+@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
 def test_roc_curve_chance_level_line(
     pyplot,
     data_binary,
@@ -148,7 +648,7 @@ def test_roc_curve_chance_level_line(
     label,
     constructor_name,
 ):
-    """Check the chance level line plotting behaviour."""
+    """Check chance level plotting behavior of `from_predictions`, `from_estimator`."""
     X, y = data_binary
 
     lr = LogisticRegression()
@@ -162,8 +662,7 @@ def test_roc_curve_chance_level_line(
             lr,
             X,
             y,
-            label=label,
-            alpha=0.8,
+            curve_kwargs={"alpha": 0.8, "label": label},
             plot_chance_level=plot_chance_level,
             chance_level_kw=chance_level_kw,
         )
@@ -171,8 +670,7 @@ def test_roc_curve_chance_level_line(
         display = RocCurveDisplay.from_predictions(
             y,
             y_score,
-            label=label,
-            alpha=0.8,
+            curve_kwargs={"alpha": 0.8, "label": label},
             plot_chance_level=plot_chance_level,
             chance_level_kw=chance_level_kw,
         )
@@ -184,32 +682,10 @@ def test_roc_curve_chance_level_line(
     assert isinstance(display.ax_, mpl.axes.Axes)
     assert isinstance(display.figure_, mpl.figure.Figure)
 
-    if plot_chance_level:
-        assert isinstance(display.chance_level_, mpl.lines.Line2D)
-        assert tuple(display.chance_level_.get_xdata()) == (0, 1)
-        assert tuple(display.chance_level_.get_ydata()) == (0, 1)
-    else:
-        assert display.chance_level_ is None
+    _check_chance_level(plot_chance_level, chance_level_kw, display)
 
-    # Checking for chance level line styles
-    if plot_chance_level and chance_level_kw is None:
-        assert display.chance_level_.get_color() == "k"
-        assert display.chance_level_.get_linestyle() == "--"
-        assert display.chance_level_.get_label() == "Chance level (AUC = 0.5)"
-    elif plot_chance_level:
-        if "c" in chance_level_kw:
-            assert display.chance_level_.get_color() == chance_level_kw["c"]
-        else:
-            assert display.chance_level_.get_color() == chance_level_kw["color"]
-        if "lw" in chance_level_kw:
-            assert display.chance_level_.get_linewidth() == chance_level_kw["lw"]
-        else:
-            assert display.chance_level_.get_linewidth() == chance_level_kw["linewidth"]
-        if "ls" in chance_level_kw:
-            assert display.chance_level_.get_linestyle() == chance_level_kw["ls"]
-        else:
-            assert display.chance_level_.get_linestyle() == chance_level_kw["linestyle"]
-        # Checking for legend behaviour
+    # Checking for legend behaviour
+    if plot_chance_level and chance_level_kw is not None:
         if label is not None or chance_level_kw.get("label") is not None:
             legend = display.ax_.get_legend()
             assert legend is not None  #  Legend should be present if any label is set
@@ -222,6 +698,62 @@ def test_roc_curve_chance_level_line(
             assert display.ax_.get_legend() is None
 
 
+@pytest.mark.parametrize("plot_chance_level", [True, False])
+@pytest.mark.parametrize(
+    "chance_level_kw",
+    [
+        None,
+        {"linewidth": 1, "color": "red", "linestyle": "-", "label": "DummyEstimator"},
+        {"lw": 1, "c": "red", "ls": "-", "label": "DummyEstimator"},
+        {"lw": 1, "color": "blue", "ls": "-", "label": None},
+    ],
+)
+@pytest.mark.parametrize("curve_kwargs", [None, {"alpha": 0.8}])
+def test_roc_curve_chance_level_line_from_cv_results(
+    pyplot,
+    data_binary,
+    plot_chance_level,
+    chance_level_kw,
+    curve_kwargs,
+):
+    """Check chance level plotting behavior with `from_cv_results`."""
+    X, y = data_binary
+    n_cv = 3
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=n_cv, return_estimator=True, return_indices=True
+    )
+
+    display = RocCurveDisplay.from_cv_results(
+        cv_results,
+        X,
+        y,
+        plot_chance_level=plot_chance_level,
+        chance_level_kwargs=chance_level_kw,
+        curve_kwargs=curve_kwargs,
+    )
+
+    import matplotlib as mpl
+
+    assert all(isinstance(line, mpl.lines.Line2D) for line in display.line_)
+    # Ensure both curve line kwargs passed correctly as well
+    if curve_kwargs:
+        assert all(line.get_alpha() == 0.8 for line in display.line_)
+    assert isinstance(display.ax_, mpl.axes.Axes)
+    assert isinstance(display.figure_, mpl.figure.Figure)
+
+    _check_chance_level(plot_chance_level, chance_level_kw, display)
+
+    legend = display.ax_.get_legend()
+    # There is always a legend, to indicate each 'Fold' curve
+    assert legend is not None
+    legend_labels = [text.get_text() for text in legend.get_texts()]
+    if plot_chance_level and chance_level_kw is not None:
+        if chance_level_kw.get("label") is not None:
+            assert chance_level_kw["label"] in legend_labels
+        else:
+            assert len(legend_labels) == 1
+
+
 @pytest.mark.parametrize(
     "clf",
     [
@@ -253,31 +785,52 @@ def test_roc_curve_display_complex_pipeline(pyplot, data_binary, clf, constructo
         name = "Classifier"
 
     assert name in display.line_.get_label()
-    assert display.estimator_name == name
+    assert display.name == name
 
 
 @pytest.mark.parametrize(
-    "roc_auc, estimator_name, expected_label",
+    "roc_auc, name, curve_kwargs, expected_labels",
     [
-        (0.9, None, "AUC = 0.90"),
-        (None, "my_est", "my_est"),
-        (0.8, "my_est2", "my_est2 (AUC = 0.80)"),
+        ([0.9, 0.8], None, None, ["AUC = 0.85 +/- 0.05", "_child1"]),
+        ([0.9, 0.8], "Est name", None, ["Est name (AUC = 0.85 +/- 0.05)", "_child1"]),
+        (
+            [0.8, 0.7],
+            ["fold1", "fold2"],
+            [{"c": "blue"}, {"c": "red"}],
+            ["fold1 (AUC = 0.80)", "fold2 (AUC = 0.70)"],
+        ),
+        (None, ["fold1", "fold2"], [{"c": "blue"}, {"c": "red"}], ["fold1", "fold2"]),
     ],
 )
 def test_roc_curve_display_default_labels(
-    pyplot, roc_auc, estimator_name, expected_label
+    pyplot, roc_auc, name, curve_kwargs, expected_labels
 ):
     """Check the default labels used in the display."""
-    fpr = np.array([0, 0.5, 1])
-    tpr = np.array([0, 0.5, 1])
-    disp = RocCurveDisplay(
-        fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=estimator_name
-    ).plot()
-    assert disp.line_.get_label() == expected_label
+    fpr = [np.array([0, 0.5, 1]), np.array([0, 0.3, 1])]
+    tpr = [np.array([0, 0.5, 1]), np.array([0, 0.3, 1])]
+    disp = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, name=name).plot(
+        curve_kwargs=curve_kwargs
+    )
+    for idx, expected_label in enumerate(expected_labels):
+        assert disp.line_[idx].get_label() == expected_label
+
+
+def _check_auc(display, constructor_name):
+    roc_auc_limit = 0.95679
+    roc_auc_limit_multi = [0.97007, 0.985915, 0.980952]
+
+    if constructor_name == "from_cv_results":
+        for idx, roc_auc in enumerate(display.roc_auc):
+            assert roc_auc == pytest.approx(roc_auc_limit_multi[idx])
+    else:
+        assert display.roc_auc == pytest.approx(roc_auc_limit)
+        assert trapezoid(display.tpr, display.fpr) == pytest.approx(roc_auc_limit)
 
 
 @pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"])
-@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
+@pytest.mark.parametrize(
+    "constructor_name", ["from_estimator", "from_predictions", "from_cv_results"]
+)
 def test_plot_roc_curve_pos_label(pyplot, response_method, constructor_name):
     # check that we can provide the positive label and display the proper
     # statistics
@@ -300,9 +853,13 @@ def test_plot_roc_curve_pos_label(pyplot, response_method, constructor_name):
 
     classifier = LogisticRegression()
     classifier.fit(X_train, y_train)
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=3, return_estimator=True, return_indices=True
+    )
 
-    # sanity check to be sure the positive class is classes_[0] and that we
-    # are betrayed by the class imbalance
+    # Sanity check to be sure the positive class is `classes_[0]`
+    # Class imbalance ensures a large difference in prediction values between classes,
+    # allowing us to catch errors when we switch `pos_label`
     assert classifier.classes_.tolist() == ["cancer", "not cancer"]
 
     y_score = getattr(classifier, response_method)(X_test)
@@ -311,43 +868,59 @@ def test_plot_roc_curve_pos_label(pyplot, response_method, constructor_name):
     y_score_cancer = -1 * y_score if y_score.ndim == 1 else y_score[:, 0]
     y_score_not_cancer = y_score if y_score.ndim == 1 else y_score[:, 1]
 
+    pos_label = "cancer"
+    y_score = y_score_cancer
     if constructor_name == "from_estimator":
         display = RocCurveDisplay.from_estimator(
             classifier,
             X_test,
             y_test,
-            pos_label="cancer",
+            pos_label=pos_label,
             response_method=response_method,
         )
-    else:
+    elif constructor_name == "from_predictions":
         display = RocCurveDisplay.from_predictions(
             y_test,
-            y_score_cancer,
-            pos_label="cancer",
+            y_score,
+            pos_label=pos_label,
+        )
+    else:
+        display = RocCurveDisplay.from_cv_results(
+            cv_results,
+            X,
+            y,
+            response_method=response_method,
+            pos_label=pos_label,
         )
 
-    roc_auc_limit = 0.95679
-
-    assert display.roc_auc == pytest.approx(roc_auc_limit)
-    assert trapezoid(display.tpr, display.fpr) == pytest.approx(roc_auc_limit)
+    _check_auc(display, constructor_name)
 
+    pos_label = "not cancer"
+    y_score = y_score_not_cancer
     if constructor_name == "from_estimator":
         display = RocCurveDisplay.from_estimator(
             classifier,
             X_test,
             y_test,
             response_method=response_method,
-            pos_label="not cancer",
+            pos_label=pos_label,
         )
-    else:
+    elif constructor_name == "from_predictions":
         display = RocCurveDisplay.from_predictions(
             y_test,
-            y_score_not_cancer,
-            pos_label="not cancer",
+            y_score,
+            pos_label=pos_label,
+        )
+    else:
+        display = RocCurveDisplay.from_cv_results(
+            cv_results,
+            X,
+            y,
+            response_method=response_method,
+            pos_label=pos_label,
         )
 
-    assert display.roc_auc == pytest.approx(roc_auc_limit)
-    assert trapezoid(display.tpr, display.fpr) == pytest.approx(roc_auc_limit)
+    _check_auc(display, constructor_name)
 
 
 # TODO(1.9): remove
@@ -381,23 +954,30 @@ def test_y_pred_deprecation_warning(pyplot):
 
 
 @pytest.mark.parametrize("despine", [True, False])
-@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
+@pytest.mark.parametrize(
+    "constructor_name", ["from_estimator", "from_predictions", "from_cv_results"]
+)
 def test_plot_roc_curve_despine(pyplot, data_binary, despine, constructor_name):
     # Check that the despine keyword is working correctly
     X, y = data_binary
 
     lr = LogisticRegression().fit(X, y)
     lr.fit(X, y)
+    cv_results = cross_validate(
+        LogisticRegression(), X, y, cv=3, return_estimator=True, return_indices=True
+    )
 
     y_pred = lr.decision_function(X)
 
-    # safe guard for the binary if/else construction
-    assert constructor_name in ("from_estimator", "from_predictions")
+    # safe guard for the if/else construction
+    assert constructor_name in ("from_estimator", "from_predictions", "from_cv_results")
 
     if constructor_name == "from_estimator":
         display = RocCurveDisplay.from_estimator(lr, X, y, despine=despine)
-    else:
+    elif constructor_name == "from_predictions":
         display = RocCurveDisplay.from_predictions(y, y_pred, despine=despine)
+    else:
+        display = RocCurveDisplay.from_cv_results(cv_results, X, y, despine=despine)
 
     for s in ["top", "right"]:
         assert display.ax_.spines[s].get_visible() is not despine
diff --git a/sklearn/utils/_plotting.py b/sklearn/utils/_plotting.py
index 946c95186374b..1a3883b7db7f5 100644
--- a/sklearn/utils/_plotting.py
+++ b/sklearn/utils/_plotting.py
@@ -1,13 +1,16 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
+import warnings
+from collections.abc import Mapping
 
 import numpy as np
 
 from . import check_consistent_length
 from ._optional_dependencies import check_matplotlib_support
 from ._response import _get_response_values_binary
+from .fixes import parse_version
 from .multiclass import type_of_target
-from .validation import _check_pos_label_consistency
+from .validation import _check_pos_label_consistency, _num_samples
 
 
 class _BinaryClassifierCurveDisplayMixin:
@@ -24,7 +27,10 @@ def _validate_plot_params(self, *, ax=None, name=None):
         if ax is None:
             _, ax = plt.subplots()
 
-        name = self.estimator_name if name is None else name
+        # Display classes are in process of changing from `estimator_name` to `name`.
+        # Try old attr name: `estimator_name` first.
+        if name is None:
+            name = getattr(self, "estimator_name", getattr(self, "name", None))
         return ax, ax.figure, name
 
     @classmethod
@@ -63,6 +69,186 @@ def _validate_from_predictions_params(
 
         return pos_label, name
 
+    @classmethod
+    def _validate_from_cv_results_params(
+        cls,
+        cv_results,
+        X,
+        y,
+        *,
+        sample_weight,
+        pos_label,
+    ):
+        check_matplotlib_support(f"{cls.__name__}.from_cv_results")
+
+        required_keys = {"estimator", "indices"}
+        if not all(key in cv_results for key in required_keys):
+            raise ValueError(
+                "`cv_results` does not contain one of the following required keys: "
+                f"{required_keys}. Set explicitly the parameters "
+                "`return_estimator=True` and `return_indices=True` to the function"
+                "`cross_validate`."
+            )
+
+        train_size, test_size = (
+            len(cv_results["indices"]["train"][0]),
+            len(cv_results["indices"]["test"][0]),
+        )
+
+        if _num_samples(X) != train_size + test_size:
+            raise ValueError(
+                "`X` does not contain the correct number of samples. "
+                f"Expected {train_size + test_size}, got {_num_samples(X)}."
+            )
+
+        if type_of_target(y) != "binary":
+            raise ValueError(
+                f"The target `y` is not binary. Got {type_of_target(y)} type of target."
+            )
+        check_consistent_length(X, y, sample_weight)
+
+        try:
+            pos_label = _check_pos_label_consistency(pos_label, y)
+        except ValueError as e:
+            # Adapt error message
+            raise ValueError(str(e).replace("y_true", "y"))
+
+        return pos_label
+
+    @staticmethod
+    def _get_legend_label(curve_legend_metric, curve_name, legend_metric_name):
+        """Helper to get legend label using `name` and `legend_metric`"""
+        if curve_legend_metric is not None and curve_name is not None:
+            label = f"{curve_name} ({legend_metric_name} = {curve_legend_metric:0.2f})"
+        elif curve_legend_metric is not None:
+            label = f"{legend_metric_name} = {curve_legend_metric:0.2f}"
+        elif curve_name is not None:
+            label = curve_name
+        else:
+            label = None
+        return label
+
+    @staticmethod
+    def _validate_curve_kwargs(
+        n_curves,
+        name,
+        legend_metric,
+        legend_metric_name,
+        curve_kwargs,
+        **kwargs,
+    ):
+        """Get validated line kwargs for each curve.
+
+        Parameters
+        ----------
+        n_curves : int
+            Number of curves.
+
+        name : list of str or None
+            Name for labeling legend entries.
+
+        legend_metric : dict
+            Dictionary with "mean" and "std" keys, or "metric" key of metric
+            values for each curve. If None, "label" will not contain metric values.
+
+        legend_metric_name : str
+            Name of the summary value provided in `legend_metrics`.
+
+        curve_kwargs : dict or list of dict or None
+            Dictionary with keywords passed to the matplotlib's `plot` function
+            to draw the individual curves. If a list is provided, the
+            parameters are applied to the curves sequentially. If a single
+            dictionary is provided, the same parameters are applied to all
+            curves.
+
+        **kwargs : dict
+            Deprecated. Keyword arguments to be passed to matplotlib's `plot`.
+        """
+        # TODO(1.9): Remove deprecated **kwargs
+        if curve_kwargs and kwargs:
+            raise ValueError(
+                "Cannot provide both `curve_kwargs` and `kwargs`. `**kwargs` is "
+                "deprecated in 1.7 and will be removed in 1.9. Pass all matplotlib "
+                "arguments to `curve_kwargs` as a dictionary."
+            )
+        if kwargs:
+            warnings.warn(
+                "`**kwargs` is deprecated and will be removed in 1.9. Pass all "
+                "matplotlib arguments to `curve_kwargs` as a dictionary instead.",
+                FutureWarning,
+            )
+            curve_kwargs = kwargs
+
+        if isinstance(curve_kwargs, list) and len(curve_kwargs) != n_curves:
+            raise ValueError(
+                f"`curve_kwargs` must be None, a dictionary or a list of length "
+                f"{n_curves}. Got: {curve_kwargs}."
+            )
+
+        # Ensure valid `name` and `curve_kwargs` combination.
+        if (
+            isinstance(name, list)
+            and len(name) != 1
+            and not isinstance(curve_kwargs, list)
+        ):
+            raise ValueError(
+                "To avoid labeling individual curves that have the same appearance, "
+                f"`curve_kwargs` should be a list of {n_curves} dictionaries. "
+                "Alternatively, set `name` to `None` or a single string to label "
+                "a single legend entry with mean ROC AUC score of all curves."
+            )
+
+        # Ensure `name` is of the correct length
+        if isinstance(name, str):
+            name = [name]
+        if isinstance(name, list) and len(name) == 1:
+            name = name * n_curves
+        name = [None] * n_curves if name is None else name
+
+        # Ensure `curve_kwargs` is of correct length
+        if isinstance(curve_kwargs, Mapping):
+            curve_kwargs = [curve_kwargs] * n_curves
+
+        default_multi_curve_kwargs = {"alpha": 0.5, "linestyle": "--", "color": "blue"}
+        if curve_kwargs is None:
+            if n_curves > 1:
+                curve_kwargs = [default_multi_curve_kwargs] * n_curves
+            else:
+                curve_kwargs = [{}]
+
+        labels = []
+        if "mean" in legend_metric:
+            label_aggregate = _BinaryClassifierCurveDisplayMixin._get_legend_label(
+                legend_metric["mean"], name[0], legend_metric_name
+            )
+            # Note: "std" always `None` when "mean" is `None` - no metric value added
+            # to label in this case
+            if legend_metric["std"] is not None:
+                # Add the "+/- std" to the end (in brackets if name provided)
+                if name[0] is not None:
+                    label_aggregate = (
+                        label_aggregate[:-1] + f" +/- {legend_metric['std']:0.2f})"
+                    )
+                else:
+                    label_aggregate = (
+                        label_aggregate + f" +/- {legend_metric['std']:0.2f}"
+                    )
+            # Add `label` for first curve only, set to `None` for remaining curves
+            labels.extend([label_aggregate] + [None] * (n_curves - 1))
+        else:
+            for curve_legend_metric, curve_name in zip(legend_metric["metric"], name):
+                labels.append(
+                    _BinaryClassifierCurveDisplayMixin._get_legend_label(
+                        curve_legend_metric, curve_name, legend_metric_name
+                    )
+                )
+
+        curve_kwargs_ = [
+            _validate_style_kwargs({"label": label}, curve_kwargs[fold_idx])
+            for fold_idx, label in enumerate(labels)
+        ]
+        return curve_kwargs_
+
 
 def _validate_score_name(score_name, scoring, negate_score):
     """Validate the `score_name` parameter.
@@ -177,3 +363,57 @@ def _despine(ax):
         ax.spines[s].set_visible(False)
     for s in ["bottom", "left"]:
         ax.spines[s].set_bounds(0, 1)
+
+
+def _deprecate_estimator_name(estimator_name, name, version):
+    """Deprecate `estimator_name` in favour of `name`."""
+    version = parse_version(version)
+    version_remove = f"{version.major}.{version.minor + 2}"
+    if estimator_name != "deprecated":
+        if name:
+            raise ValueError(
+                "Cannot provide both `estimator_name` and `name`. `estimator_name` "
+                f"is deprecated in {version} and will be removed in {version_remove}. "
+                "Use `name` only."
+            )
+        warnings.warn(
+            f"`estimator_name` is deprecated in {version} and will be removed in "
+            f"{version_remove}. Use `name` instead.",
+            FutureWarning,
+        )
+        return estimator_name
+    return name
+
+
+def _convert_to_list_leaving_none(param):
+    """Convert parameters to a list, leaving `None` as is."""
+    if param is None:
+        return None
+    if isinstance(param, list):
+        return param
+    return [param]
+
+
+def _check_param_lengths(required, optional, class_name):
+    """Check required and optional parameters are of the same length."""
+    optional_provided = {}
+    for name, param in optional.items():
+        if isinstance(param, list):
+            optional_provided[name] = param
+
+    all_params = {**required, **optional_provided}
+    if len({len(param) for param in all_params.values()}) > 1:
+        param_keys = [key for key in all_params.keys()]
+        # Note: below code requires `len(param_keys) >= 2`, which is the case for all
+        # display classes
+        params_formatted = " and ".join([", ".join(param_keys[:-1]), param_keys[-1]])
+        or_plot = ""
+        if "'name' (or self.name)" in param_keys:
+            or_plot = " (or `plot`)"
+        lengths_formatted = ", ".join(
+            f"{key}: {len(value)}" for key, value in all_params.items()
+        )
+        raise ValueError(
+            f"{params_formatted} from `{class_name}` initialization{or_plot}, "
+            f"should all be lists of the same length. Got: {lengths_formatted}"
+        )
diff --git a/sklearn/utils/tests/test_plotting.py b/sklearn/utils/tests/test_plotting.py
index 5f0287c1d0d66..db2f797ac2547 100644
--- a/sklearn/utils/tests/test_plotting.py
+++ b/sklearn/utils/tests/test_plotting.py
@@ -4,6 +4,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.utils._plotting import (
     _BinaryClassifierCurveDisplayMixin,
+    _deprecate_estimator_name,
     _despine,
     _interval_max_min_ratio,
     _validate_score_name,
@@ -117,6 +118,273 @@ def test_validate_from_predictions_params_returns(pyplot, name, pos_label, y_tru
     assert pos_label_out == expected_pos_label
 
 
+@pytest.mark.parametrize(
+    "params, err_msg",
+    [
+        (
+            {
+                # Missing "indices" key
+                "cv_results": {"estimator": "dummy"},
+                "X": np.array([[1, 2], [3, 4]]),
+                "y": np.array([0, 1]),
+                "sample_weight": None,
+                "pos_label": None,
+            },
+            "`cv_results` does not contain one of the following",
+        ),
+        (
+            {
+                "cv_results": {
+                    "estimator": "dummy",
+                    "indices": {"test": [[1, 2], [1, 2]], "train": [[3, 4], [3, 4]]},
+                },
+                # `X` wrong length
+                "X": np.array([[1, 2]]),
+                "y": np.array([0, 1]),
+                "sample_weight": None,
+                "pos_label": None,
+            },
+            "`X` does not contain the correct number of",
+        ),
+        (
+            {
+                "cv_results": {
+                    "estimator": "dummy",
+                    "indices": {"test": [[1, 2], [1, 2]], "train": [[3, 4], [3, 4]]},
+                },
+                "X": np.array([1, 2, 3, 4]),
+                # `y` not binary
+                "y": np.array([0, 2, 1, 3]),
+                "sample_weight": None,
+                "pos_label": None,
+            },
+            "The target `y` is not binary",
+        ),
+        (
+            {
+                "cv_results": {
+                    "estimator": "dummy",
+                    "indices": {"test": [[1, 2], [1, 2]], "train": [[3, 4], [3, 4]]},
+                },
+                "X": np.array([1, 2, 3, 4]),
+                "y": np.array([0, 1, 0, 1]),
+                # `sample_weight` wrong length
+                "sample_weight": np.array([0.5]),
+                "pos_label": None,
+            },
+            "Found input variables with inconsistent",
+        ),
+        (
+            {
+                "cv_results": {
+                    "estimator": "dummy",
+                    "indices": {"test": [[1, 2], [1, 2]], "train": [[3, 4], [3, 4]]},
+                },
+                "X": np.array([1, 2, 3, 4]),
+                "y": np.array([2, 3, 2, 3]),
+                "sample_weight": None,
+                # Not specified when `y` not in {0, 1} or {-1, 1}
+                "pos_label": None,
+            },
+            "y takes value in {2, 3} and pos_label is not specified",
+        ),
+    ],
+)
+def test_validate_from_cv_results_params(pyplot, params, err_msg):
+    """Check parameter validation is performed correctly."""
+    with pytest.raises(ValueError, match=err_msg):
+        _BinaryClassifierCurveDisplayMixin()._validate_from_cv_results_params(**params)
+
+
+@pytest.mark.parametrize(
+    "curve_legend_metric, curve_name, expected_label",
+    [
+        (0.85, None, "AUC = 0.85"),
+        (None, "Model A", "Model A"),
+        (0.95, "Random Forest", "Random Forest (AUC = 0.95)"),
+        (None, None, None),
+    ],
+)
+def test_get_legend_label(curve_legend_metric, curve_name, expected_label):
+    """Check `_get_legend_label` returns the correct label."""
+    legend_metric_name = "AUC"
+    label = _BinaryClassifierCurveDisplayMixin._get_legend_label(
+        curve_legend_metric, curve_name, legend_metric_name
+    )
+    assert label == expected_label
+
+
+# TODO(1.9) : Remove
+@pytest.mark.parametrize("curve_kwargs", [{"alpha": 1.0}, None])
+@pytest.mark.parametrize("kwargs", [{}, {"alpha": 1.0}])
+def test_validate_curve_kwargs_deprecate_kwargs(curve_kwargs, kwargs):
+    """Check `_validate_curve_kwargs` deprecates kwargs correctly."""
+    n_curves = 1
+    name = None
+    legend_metric = {"mean": 0.8, "std": 0.1}
+    legend_metric_name = "AUC"
+
+    if curve_kwargs and kwargs:
+        with pytest.raises(ValueError, match="Cannot provide both `curve_kwargs`"):
+            _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+                n_curves,
+                name,
+                legend_metric,
+                legend_metric_name,
+                curve_kwargs,
+                **kwargs,
+            )
+    elif kwargs:
+        with pytest.warns(FutureWarning, match=r"`\*\*kwargs` is deprecated and"):
+            _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+                n_curves,
+                name,
+                legend_metric,
+                legend_metric_name,
+                curve_kwargs,
+                **kwargs,
+            )
+    else:
+        # No warning or error should be raised
+        _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+            n_curves, name, legend_metric, legend_metric_name, curve_kwargs, **kwargs
+        )
+
+
+def test_validate_curve_kwargs_error():
+    """Check `_validate_curve_kwargs` performs parameter validation correctly."""
+    n_curves = 3
+    legend_metric = {"mean": 0.8, "std": 0.1}
+    legend_metric_name = "AUC"
+    with pytest.raises(ValueError, match="`curve_kwargs` must be None"):
+        _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+            n_curves=n_curves,
+            name=None,
+            legend_metric=legend_metric,
+            legend_metric_name=legend_metric_name,
+            curve_kwargs=[{"alpha": 1.0}],
+        )
+    with pytest.raises(ValueError, match="To avoid labeling individual curves"):
+        name = ["one", "two", "three"]
+        _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+            n_curves=n_curves,
+            name=name,
+            legend_metric=legend_metric,
+            legend_metric_name=legend_metric_name,
+            curve_kwargs=None,
+        )
+        _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+            n_curves=n_curves,
+            name=name,
+            legend_metric=legend_metric,
+            legend_metric_name=legend_metric_name,
+            curve_kwargs={"alpha": 1.0},
+        )
+
+
+@pytest.mark.parametrize("name", [None, "curve_name", ["curve_name"]])
+@pytest.mark.parametrize(
+    "legend_metric",
+    [
+        {"mean": 0.8, "std": 0.2},
+        {"mean": None, "std": None},
+    ],
+)
+@pytest.mark.parametrize("legend_metric_name", ["AUC", "AP"])
+@pytest.mark.parametrize(
+    "curve_kwargs",
+    [
+        None,
+        {"color": "red"},
+    ],
+)
+def test_validate_curve_kwargs_single_legend(
+    name, legend_metric, legend_metric_name, curve_kwargs
+):
+    """Check `_validate_curve_kwargs` returns correct kwargs for single legend entry."""
+    n_curves = 3
+    curve_kwargs_out = _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+        n_curves=n_curves,
+        name=name,
+        legend_metric=legend_metric,
+        legend_metric_name=legend_metric_name,
+        curve_kwargs=curve_kwargs,
+    )
+
+    assert isinstance(curve_kwargs_out, list)
+    assert len(curve_kwargs_out) == n_curves
+
+    expected_label = None
+    if isinstance(name, list):
+        name = name[0]
+    if name is not None:
+        expected_label = name
+        if legend_metric["mean"] is not None:
+            expected_label = expected_label + f" ({legend_metric_name} = 0.80 +/- 0.20)"
+    # `name` is None
+    elif legend_metric["mean"] is not None:
+        expected_label = f"{legend_metric_name} = 0.80 +/- 0.20"
+
+    assert curve_kwargs_out[0]["label"] == expected_label
+    # All remaining curves should have None as "label"
+    assert curve_kwargs_out[1]["label"] is None
+    assert curve_kwargs_out[2]["label"] is None
+
+    # Default multi-curve kwargs
+    if curve_kwargs is None:
+        assert all(len(kwargs) == 4 for kwargs in curve_kwargs_out)
+        assert all(kwargs["alpha"] == 0.5 for kwargs in curve_kwargs_out)
+        assert all(kwargs["linestyle"] == "--" for kwargs in curve_kwargs_out)
+        assert all(kwargs["color"] == "blue" for kwargs in curve_kwargs_out)
+    else:
+        assert all(len(kwargs) == 2 for kwargs in curve_kwargs_out)
+        assert all(kwargs["color"] == "red" for kwargs in curve_kwargs_out)
+
+
+@pytest.mark.parametrize("name", [None, "curve_name", ["one", "two", "three"]])
+@pytest.mark.parametrize(
+    "legend_metric", [{"metric": [1.0, 1.0, 1.0]}, {"metric": [None, None, None]}]
+)
+@pytest.mark.parametrize("legend_metric_name", ["AUC", "AP"])
+def test_validate_curve_kwargs_multi_legend(name, legend_metric, legend_metric_name):
+    """Check `_validate_curve_kwargs` returns correct kwargs for multi legend entry."""
+    n_curves = 3
+    curve_kwargs = [{"color": "red"}, {"color": "yellow"}, {"color": "blue"}]
+    curve_kwargs_out = _BinaryClassifierCurveDisplayMixin._validate_curve_kwargs(
+        n_curves=n_curves,
+        name=name,
+        legend_metric=legend_metric,
+        legend_metric_name=legend_metric_name,
+        curve_kwargs=curve_kwargs,
+    )
+
+    assert isinstance(curve_kwargs_out, list)
+    assert len(curve_kwargs_out) == n_curves
+
+    expected_labels = [None, None, None]
+    if isinstance(name, str):
+        expected_labels = "curve_name"
+        if legend_metric["metric"][0] is not None:
+            expected_labels = expected_labels + f" ({legend_metric_name} = 1.00)"
+        expected_labels = [expected_labels] * n_curves
+    elif isinstance(name, list) and legend_metric["metric"][0] is None:
+        expected_labels = name
+    elif isinstance(name, list) and legend_metric["metric"][0] is not None:
+        expected_labels = [
+            f"{name_single} ({legend_metric_name} = 1.00)" for name_single in name
+        ]
+    # `name` is None
+    elif legend_metric["metric"][0] is not None:
+        expected_labels = [f"{legend_metric_name} = 1.00"] * n_curves
+
+    for idx, expected_label in enumerate(expected_labels):
+        assert curve_kwargs_out[idx]["label"] == expected_label
+
+    assert all(len(kwargs) == 2 for kwargs in curve_kwargs_out)
+    for curve_kwarg, curve_kwarg_out in zip(curve_kwargs, curve_kwargs_out):
+        assert curve_kwarg_out["color"] == curve_kwarg["color"]
+
+
 def metric():
     pass  # pragma: no cover
 
@@ -246,3 +514,31 @@ def test_despine(pyplot):
     assert ax.spines["right"].get_visible() is False
     assert ax.spines["bottom"].get_bounds() == (0, 1)
     assert ax.spines["left"].get_bounds() == (0, 1)
+
+
+@pytest.mark.parametrize("estimator_name", ["my_est_name", "deprecated"])
+@pytest.mark.parametrize("name", [None, "my_name"])
+def test_deprecate_estimator_name(estimator_name, name):
+    """Check `_deprecate_estimator_name` behaves correctly"""
+    version = "1.7"
+    version_remove = "1.9"
+
+    if estimator_name == "deprecated":
+        name_out = _deprecate_estimator_name(estimator_name, name, version)
+        assert name_out == name
+    # `estimator_name` is provided and `name` is:
+    elif name is None:
+        warning_message = (
+            f"`estimator_name` is deprecated in {version} and will be removed in "
+            f"{version_remove}. Use `name` instead."
+        )
+        with pytest.warns(FutureWarning, match=warning_message):
+            result = _deprecate_estimator_name(estimator_name, name, version)
+        assert result == estimator_name
+    elif name is not None:
+        error_message = (
+            f"Cannot provide both `estimator_name` and `name`. `estimator_name` "
+            f"is deprecated in {version} and will be removed in {version_remove}. "
+        )
+        with pytest.raises(ValueError, match=error_message):
+            _deprecate_estimator_name(estimator_name, name, version)

From 2fe9a2d4e8b30e580373a670cc76c2e778d5661d Mon Sep 17 00:00:00 2001
From: scikit-learn-bot 
Date: Tue, 27 May 2025 10:37:59 +0200
Subject: [PATCH 46/64] :lock: :robot: CI Update lock files for free-threaded
 CI build(s) :lock: :robot: (#31428)

Co-authored-by: Lock file bot 
---
 .../azure/pylatest_free_threaded_linux-64_conda.lock      | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock
index c77b64e6d4d66..a75f20be093c2 100644
--- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock
@@ -34,22 +34,22 @@ https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d
 https://conda.anaconda.org/conda-forge/linux-64/python-3.13.3-h4724d56_1_cp313t.conda#8193603fe48ace3d8801cfb246f44491
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7
 https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.3-py313hd8ed1ab_1.conda#6ba9ba47b91b7758cb963d0f0eaf3422
-https://conda.anaconda.org/conda-forge/noarch/cython-3.1.0-pyh2c78169_101.conda#3db6841854d296919c98e870276d0377
+https://conda.anaconda.org/conda-forge/noarch/cython-3.1.1-pyh2c78169_101.conda#f085516359786c4fea51bf05227ef3d2
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90
 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108
 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openblas.conda#728dbebd0f7a20337218beacffd37916
 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a
-https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda#8e25221b702272394b86b0f4d7217f77
+https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162
 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9
 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714
 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971
-https://conda.anaconda.org/conda-forge/noarch/setuptools-80.1.0-pyhff2d567_0.conda#f6f72d0837c79eaec77661be43e8a691
+https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215
 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.13.2-pyh29332c3_0.conda#83fc6ae00127671e301c9f44254c31b8
 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.0-pyhd8ed1ab_0.conda#3d7257f0a61c9aa4ffa3e324a887416b
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c
 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-31_he106b2a_openblas.conda#abb32c727da370c481a1c206f5159ce9
 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda#452b98eafe050ecff932f0ec832dd03f
 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b

From 68a280709ad5b7f9c359863b3cf2ffd6a7ce551e Mon Sep 17 00:00:00 2001
From: scikit-learn-bot 
Date: Tue, 27 May 2025 10:38:54 +0200
Subject: [PATCH 47/64] :lock: :robot: CI Update lock files for array-api CI
 build(s) :lock: :robot: (#31427)

Co-authored-by: Lock file bot 
---
 ...a_forge_cuda_array-api_linux-64_conda.lock | 39 +++++++++----------
 1 file changed, 19 insertions(+), 20 deletions(-)

diff --git a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock
index 2acb45fbbe9c5..78846708d0a03 100644
--- a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock
+++ b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock
@@ -53,7 +53,6 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.3-h3870646_2.
 https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.3-h3870646_2.conda#303d9e83e0518f1dcb66e90054635ca6
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553
 https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.1-h5888daf_0.conda#bfd56492d8346d669010eccafe0ba058
-https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.0-h5888daf_0.conda#d6845ae4dea52a2f90178bf1829a21f8
 https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda#d411fc29e338efb48c5fd4576d71d881
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3
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@@ -101,7 +100,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.3-h6128344_1.co
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+https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.5.1-pyhd8ed1ab_0.conda#2d2c9ef879a7e64e2dc657b09272c2b6
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@@ -151,12 +150,12 @@ https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda
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-https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5-py313h536fd9c_0.conda#922c4f97a250416414fc393acf5fe53c
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@@ -167,24 +166,24 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.6-hd08a7f5_4.cond
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-https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d
+https://conda.anaconda.org/conda-forge/linux-64/coverage-7.8.2-py313h8060acc_0.conda#b278629953bd3424060870fca744de4a
+https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7
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-https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.9.2-h65c71a3_0.conda#d045b1d878031eb497cab44e6392b1df
+https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a
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-https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.9-he970967_0.conda#ca2de8bbdc871bce41dbf59e51324165
+https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.10-he970967_0.conda#2e5bf4f1da39c0b32778561c3c4e5878
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@@ -201,11 +200,11 @@ https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_
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-https://conda.anaconda.org/conda-forge/linux-64/cudnn-9.10.0.56-h0fdc2d1_1.conda#41596f6e8efb39da98aa320df5635a15
+https://conda.anaconda.org/conda-forge/linux-64/cudnn-9.10.1.4-haad7af6_0.conda#8382d957333e0d3280dcbf5691516dc1
 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee
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-https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_0.conda#79a1be1cd92a7f2b62e6c0a7c2da8bf8
-https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_0.conda#9a912cce23df3fea9d2adb75e505b153
+https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_1.conda#330b1dadfa7c3205a01fa9599fabe808
+https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_1.conda#12117145218e7e1a528c8396ed803058
 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.36.0-h2b5623c_0.conda#c96ca58ad3352a964bfcb85de6cd1496
 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-31_he2f377e_openblas.conda#7e5fff7d0db69be3a266f7e79a3bb0e2
 https://conda.anaconda.org/conda-forge/linux-64/libmagma-2.9.0-h45b15fe_0.conda#703a1ab01e36111d8bb40bc7517e900b
@@ -227,7 +226,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.36.0-h
 https://conda.anaconda.org/conda-forge/linux-64/libmagma_sparse-2.9.0-h45b15fe_0.conda#beac0a5bbe0af75db6b16d3d8fd24f7e
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 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py313ha87cce1_3.conda#6248b529e537b1d4cb5ab3ef7f537795
-https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.29.0-py39hfac2b71_1.conda#3c9014d11acfd00121c3d275aea778ad
+https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832
 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.1.1-pyhd8ed1ab_0.conda#1e35d8f975bc0e984a19819aa91c440a
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@@ -239,7 +238,7 @@ https://conda.anaconda.org/conda-forge/linux-64/cupy-13.4.1-py313h66a2ee2_0.cond
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 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.3-py313h129903b_0.conda#4f8816d006b1c155ec416bcf7ff6cee2
-https://conda.anaconda.org/conda-forge/linux-64/polars-1.29.0-default_h9d2e075_1.conda#7482bbd35de40c380fd2aa07c4babf90
+https://conda.anaconda.org/conda-forge/linux-64/polars-1.30.0-default_h1443d73_0.conda#19698b29e8544d2dd615699826037039
 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py313hf0ab243_1.conda#4c769bf3858f424cb2ecf952175ec600
 https://conda.anaconda.org/conda-forge/linux-64/libarrow-19.0.1-hc7b3859_3_cpu.conda#9ed3ded6da29dec8417f2e1db68798f2
 https://conda.anaconda.org/conda-forge/linux-64/pytorch-2.4.1-cuda118_mkl_py313_h909c4c2_306.conda#de6e45613bbdb51127e9ff483c31bf41

From a238e4ddaf0af264cef6eabf1d86578cecab9265 Mon Sep 17 00:00:00 2001
From: scikit-learn-bot 
Date: Tue, 27 May 2025 10:39:22 +0200
Subject: [PATCH 48/64] :lock: :robot: CI Update lock files for scipy-dev CI
 build(s) :lock: :robot: (#31426)

Co-authored-by: Lock file bot 
---
 build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
index 06f78703619e1..4aa3536528c84 100644
--- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
@@ -32,14 +32,14 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2
 # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2
 # pip certifi @ https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl#sha256=30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3
 # pip charset-normalizer @ https://files.pythonhosted.org/packages/e2/28/ffc026b26f441fc67bd21ab7f03b313ab3fe46714a14b516f931abe1a2d8/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c
-# pip coverage @ https://files.pythonhosted.org/packages/cb/74/2f8cc196643b15bc096d60e073691dadb3dca48418f08bc78dd6e899383e/coverage-7.8.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5aaeb00761f985007b38cf463b1d160a14a22c34eb3f6a39d9ad6fc27cb73008
+# pip coverage @ https://files.pythonhosted.org/packages/89/60/f5f50f61b6332451520e6cdc2401700c48310c64bc2dd34027a47d6ab4ca/coverage-7.8.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=dc67994df9bcd7e0150a47ef41278b9e0a0ea187caba72414b71dc590b99a108
 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2
 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc
 # pip idna @ https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl#sha256=946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3
 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b
 # pip iniconfig @ https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl#sha256=9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760
 # pip markupsafe @ https://files.pythonhosted.org/packages/0c/91/96cf928db8236f1bfab6ce15ad070dfdd02ed88261c2afafd4b43575e9e9/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=15ab75ef81add55874e7ab7055e9c397312385bd9ced94920f2802310c930396
-# pip meson @ https://files.pythonhosted.org/packages/df/d7/f1c8acf0e597d4d07532f519780ee6e11ba285a9b092f18706b4c9118331/meson-1.8.0-py3-none-any.whl#sha256=472b7b25da286447333d32872b82d1c6f1a34024fb8ee017d7308056c25fec1f
+# pip meson @ https://files.pythonhosted.org/packages/46/77/726b14be352aa6911e206ca7c4d95c5be49660604dfee0bfed0fc75823e5/meson-1.8.1-py3-none-any.whl#sha256=374bbf71247e629475fc10b0bd2ef66fc418c2d8f4890572f74de0f97d0d42da
 # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0
 # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484
 # pip platformdirs @ https://files.pythonhosted.org/packages/fe/39/979e8e21520d4e47a0bbe349e2713c0aac6f3d853d0e5b34d76206c439aa/platformdirs-4.3.8-py3-none-any.whl#sha256=ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4

From 7642ce81424e0b1eb4acc3f6f7462a32a888d467 Mon Sep 17 00:00:00 2001
From: scikit-learn-bot 
Date: Tue, 27 May 2025 12:00:49 +0200
Subject: [PATCH 49/64] :lock: :robot: CI Update lock files for main CI
 build(s) :lock: :robot: (#31429)

Co-authored-by: Lock file bot 
Co-authored-by: Olivier Grisel 
---
 build_tools/azure/debian_32bit_lock.txt       |  8 ++--
 ...latest_conda_forge_mkl_linux-64_conda.lock | 33 ++++++++--------
 ...pylatest_conda_forge_mkl_osx-64_conda.lock | 20 +++++-----
 ...test_conda_mkl_no_openmp_osx-64_conda.lock |  6 +--
 ...st_pip_openblas_pandas_linux-64_conda.lock | 10 ++---
 .../pymin_conda_forge_mkl_win-64_conda.lock   | 16 ++++----
 ...nblas_min_dependencies_linux-64_conda.lock | 25 ++++++------
 ...e_openblas_ubuntu_2204_linux-64_conda.lock | 10 ++---
 build_tools/azure/ubuntu_atlas_lock.txt       |  2 +-
 build_tools/circle/doc_linux-64_conda.lock    | 39 +++++++++----------
 .../doc_min_dependencies_linux-64_conda.lock  | 35 ++++++++---------
 ...n_conda_forge_arm_linux-aarch64_conda.lock | 23 ++++++-----
 12 files changed, 111 insertions(+), 116 deletions(-)

diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt
index c0a25641cc589..c7b8cbceccacb 100644
--- a/build_tools/azure/debian_32bit_lock.txt
+++ b/build_tools/azure/debian_32bit_lock.txt
@@ -4,15 +4,15 @@
 #
 #    pip-compile --output-file=build_tools/azure/debian_32bit_lock.txt build_tools/azure/debian_32bit_requirements.txt
 #
-coverage[toml]==7.8.0
+coverage[toml]==7.8.2
     # via pytest-cov
-cython==3.1.0
+cython==3.1.1
     # via -r build_tools/azure/debian_32bit_requirements.txt
 iniconfig==2.1.0
     # via pytest
-joblib==1.5.0
+joblib==1.5.1
     # via -r build_tools/azure/debian_32bit_requirements.txt
-meson==1.8.0
+meson==1.8.1
     # via meson-python
 meson-python==0.18.0
     # via -r build_tools/azure/debian_32bit_requirements.txt
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
index ee1762223730b..b53cd9ad6a1a7 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
@@ -51,7 +51,6 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.3-hafb2847_5.
 https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.7-hafb2847_1.conda#6d28d50637fac4f081a0903b4b33d56d
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553
 https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.1-h5888daf_0.conda#bfd56492d8346d669010eccafe0ba058
-https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.0-h5888daf_0.conda#d6845ae4dea52a2f90178bf1829a21f8
 https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda#d411fc29e338efb48c5fd4576d71d881
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3
 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344155d33912347b37f0ae6c410a835
@@ -113,22 +112,22 @@ https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#
 https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.3-py313hd8ed1ab_101.conda#904a822cbd380adafb9070debf8579a8
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-https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.0-py313h5dec8f5_1.conda#43ad5286d089949501cf07064693d070
+https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.1-py313h5dec8f5_1.conda#f114755cdd37627732b1884b7b15d4b5
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90
 https://conda.anaconda.org/conda-forge/noarch/filelock-3.18.0-pyhd8ed1ab_0.conda#4547b39256e296bb758166893e909a7c
-https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.3.2-pyhd8ed1ab_0.conda#9c40692c3d24c7aaf335f673ac09d308
+https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.5.1-pyhd8ed1ab_0.conda#2d2c9ef879a7e64e2dc657b09272c2b6
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 https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.13.0-h332b0f4_0.conda#cbdc92ac0d93fe3c796e36ad65c7905c
 https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669
-https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.1-h3618099_1.conda#714c97d4ff495ab69d1fdfcadbcae985
+https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.2-h3618099_0.conda#072ab14a02164b7c0c089055368ff776
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 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda#14dbe05b929e329dbaa6f2d0aa19466d
 https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py313h8060acc_1.conda#21b62c55924f01b6eef6827167b46acb
-https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda#8e25221b702272394b86b0f4d7217f77
+https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162
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@@ -138,7 +137,7 @@ https://conda.anaconda.org/conda-forge/linux-64/orc-2.1.2-h17f744e_0.conda#ef7f9
 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9
 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714
 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971
-https://conda.anaconda.org/conda-forge/noarch/pybind11-global-2.13.6-pyh415d2e4_2.conda#120541563e520d12d8e39abd7de9092c
+https://conda.anaconda.org/conda-forge/noarch/pybind11-global-2.13.6-pyh217bc35_3.conda#730a5284e26d6bdb73332dafb26aec82
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda#513d3c262ee49b54a8fec85c5bc99764
 https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda#88476ae6ebd24f39261e0854ac244f33
 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960
@@ -148,7 +147,7 @@ https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f
 https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215
-https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5-py313h536fd9c_0.conda#922c4f97a250416414fc393acf5fe53c
+https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.1-py313h536fd9c_0.conda#e9434a5155db25c38ade26f71a2f5a48
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 https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.44-hb9d3cd8_0.conda#7c91bfc90672888259675ad2ad28af9c
@@ -159,24 +158,24 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.0-h59ae206_7.cond
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.13.0-h35de22e_3.conda#df3ea458761b3fdf9e6eb7d8a38c121a
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-https://conda.anaconda.org/conda-forge/linux-64/coverage-7.8.0-py313h8060acc_0.conda#375064d30e709bf7c1d4580e70aaea61
-https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d
+https://conda.anaconda.org/conda-forge/linux-64/coverage-7.8.2-py313h8060acc_0.conda#b278629953bd3424060870fca744de4a
+https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7
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-https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.0-pyhd8ed1ab_0.conda#3d7257f0a61c9aa4ffa3e324a887416b
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c
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-https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.9-he970967_0.conda#ca2de8bbdc871bce41dbf59e51324165
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-https://conda.anaconda.org/conda-forge/noarch/pybind11-2.13.6-pyh1ec8472_2.conda#8088a5e7b2888c780738c3130f2a969d
+https://conda.anaconda.org/conda-forge/noarch/pybind11-2.13.6-pyhc790b64_3.conda#1594696beebf1ecb6d29a1136f859a74
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@@ -194,8 +193,8 @@ https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e6
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+https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_1.conda#330b1dadfa7c3205a01fa9599fabe808
+https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_1.conda#12117145218e7e1a528c8396ed803058
 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.36.0-hc4361e1_1.conda#ae36e6296a8dd8e8a9a8375965bf6398
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@@ -233,14 +232,14 @@ https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_hcf00494_mkl
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+https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832
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+https://conda.anaconda.org/conda-forge/linux-64/polars-1.30.0-default_h1443d73_0.conda#19698b29e8544d2dd615699826037039
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diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
index ab2cd2c095474..c91fc7eae1452 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
@@ -59,19 +59,19 @@ https://conda.anaconda.org/conda-forge/osx-64/sigtool-0.1.3-h88f4db0_0.tar.bz2#f
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+https://conda.anaconda.org/conda-forge/osx-64/cython-3.1.1-py313h9efc8c2_1.conda#b94bca8fec5fbaa69375656928e05c1d
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+https://conda.anaconda.org/conda-forge/osx-64/libclang-cpp18.1-18.1.8-default_h3571c67_10.conda#bf6753267e6f848f369c5bc2373dddd6
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-https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda#8e25221b702272394b86b0f4d7217f77
+https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162
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@@ -81,21 +81,21 @@ https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7d
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-https://conda.anaconda.org/conda-forge/noarch/setuptools-80.1.0-pyhff2d567_0.conda#f6f72d0837c79eaec77661be43e8a691
+https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c
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+https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c
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@@ -105,7 +105,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1a
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+https://conda.anaconda.org/conda-forge/osx-64/clang-18.1.8-default_h576c50e_10.conda#350a10c62423982b0c80a043b9921c00
 https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.3.2-py313ha0b1807_0.conda#2c2d1f840df1c512b34e0537ef928169
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@@ -113,7 +113,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.5-pyhd8ed1ab_0.conda#c3
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-https://conda.anaconda.org/conda-forge/osx-64/clangxx-18.1.8-default_heb2e8d1_9.conda#4ba6bd39da787a7306eba77555e86dd3
+https://conda.anaconda.org/conda-forge/osx-64/clangxx-18.1.8-default_heb2e8d1_10.conda#c39251c90faf5ba495d9f9ef88d7563e
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diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
index 8716bbf973504..7be311177e65f 100644
--- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
@@ -53,7 +53,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b2
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-https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.4.2-py312h46256e1_0.conda#6b41d7d8a2bf93ae3fc512202b14a9ec
+https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.5-py312h46256e1_0.conda#7e82973ed53e71854971e7b3922fad24
 https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h46256e1_1.conda#4a7fd1dec7277c8ab71aa11aa08df86b
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@@ -75,8 +75,8 @@ https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.7-py312hac873b0_0.conda#6
 https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d57b4c21a9261f97fa511e0940c5d93
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 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-5.2.1-py312h1962661_0.conda#58881950d4ce74c9302b56961f97a43c
-# pip cython @ https://files.pythonhosted.org/packages/e9/64/ae1d8848550ec3975634fcf189ccc85e73c3b9f76369dd85c484f2f8f1c3/cython-3.1.0-cp312-cp312-macosx_10_13_x86_64.whl#sha256=8f8c4753f6b926046c0cdf6037ba8560f6677730bf0ab9c1db4e0163b4bb30f9
-# pip meson @ https://files.pythonhosted.org/packages/df/d7/f1c8acf0e597d4d07532f519780ee6e11ba285a9b092f18706b4c9118331/meson-1.8.0-py3-none-any.whl#sha256=472b7b25da286447333d32872b82d1c6f1a34024fb8ee017d7308056c25fec1f
+# pip cython @ https://files.pythonhosted.org/packages/78/06/83ff82381319ff68ae46f9dd3024b1d5101997e81a8e955811525b6f934b/cython-3.1.1-cp312-cp312-macosx_10_13_x86_64.whl#sha256=9d7dc0e4d0cd491fac679a61e9ede348c64ca449f99a284f9a01851aa1dbc7f6
+# pip meson @ https://files.pythonhosted.org/packages/46/77/726b14be352aa6911e206ca7c4d95c5be49660604dfee0bfed0fc75823e5/meson-1.8.1-py3-none-any.whl#sha256=374bbf71247e629475fc10b0bd2ef66fc418c2d8f4890572f74de0f97d0d42da
 # pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb
 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad
 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2
diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
index 9e455156b43d5..81a109c63758c 100644
--- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
@@ -32,19 +32,19 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2
 # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2
 # pip certifi @ https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl#sha256=30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3
 # pip charset-normalizer @ https://files.pythonhosted.org/packages/e2/28/ffc026b26f441fc67bd21ab7f03b313ab3fe46714a14b516f931abe1a2d8/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c
-# pip coverage @ https://files.pythonhosted.org/packages/cb/74/2f8cc196643b15bc096d60e073691dadb3dca48418f08bc78dd6e899383e/coverage-7.8.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5aaeb00761f985007b38cf463b1d160a14a22c34eb3f6a39d9ad6fc27cb73008
+# pip coverage @ https://files.pythonhosted.org/packages/89/60/f5f50f61b6332451520e6cdc2401700c48310c64bc2dd34027a47d6ab4ca/coverage-7.8.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=dc67994df9bcd7e0150a47ef41278b9e0a0ea187caba72414b71dc590b99a108
 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30
-# pip cython @ https://files.pythonhosted.org/packages/8f/14/3676fcf2936c3a01538c01069f649440d3948d77ac117934896ed20f724b/cython-3.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c088ac33f4fa04b3589c4e5cfb8a81e9d9a990405409f9c8bfab0f5a9e8b724f
+# pip cython @ https://files.pythonhosted.org/packages/ca/90/9fe8b93fa239b4871252274892c232415f53d5af0859c4a6ac9b1cbf9950/cython-3.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=7da069ca769903c5dee56c5f7ab47b2b7b91030eee48912630db5f4f3ec5954a
 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2
 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc
 # pip fonttools @ https://files.pythonhosted.org/packages/60/49/aaecb1b3cea2b9b9c7cea6240d6bc8090feb5489a6fbf93cb68003be979b/fonttools-4.58.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=2ceef6f6ab58061a811967e3e32e630747fcb823dcc33a9a2c80e2d0d17cb292
 # pip idna @ https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl#sha256=946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3
 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b
 # pip iniconfig @ https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl#sha256=9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760
-# pip joblib @ https://files.pythonhosted.org/packages/da/d3/13ee227a148af1c693654932b8b0b02ed64af5e1f7406d56b088b57574cd/joblib-1.5.0-py3-none-any.whl#sha256=206144b320246485b712fc8cc51f017de58225fa8b414a1fe1764a7231aca491
+# pip joblib @ https://files.pythonhosted.org/packages/7d/4f/1195bbac8e0c2acc5f740661631d8d750dc38d4a32b23ee5df3cde6f4e0d/joblib-1.5.1-py3-none-any.whl#sha256=4719a31f054c7d766948dcd83e9613686b27114f190f717cec7eaa2084f8a74a
 # pip kiwisolver @ https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246
 # pip markupsafe @ https://files.pythonhosted.org/packages/0c/91/96cf928db8236f1bfab6ce15ad070dfdd02ed88261c2afafd4b43575e9e9/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=15ab75ef81add55874e7ab7055e9c397312385bd9ced94920f2802310c930396
-# pip meson @ https://files.pythonhosted.org/packages/df/d7/f1c8acf0e597d4d07532f519780ee6e11ba285a9b092f18706b4c9118331/meson-1.8.0-py3-none-any.whl#sha256=472b7b25da286447333d32872b82d1c6f1a34024fb8ee017d7308056c25fec1f
+# pip meson @ https://files.pythonhosted.org/packages/46/77/726b14be352aa6911e206ca7c4d95c5be49660604dfee0bfed0fc75823e5/meson-1.8.1-py3-none-any.whl#sha256=374bbf71247e629475fc10b0bd2ef66fc418c2d8f4890572f74de0f97d0d42da
 # pip networkx @ https://files.pythonhosted.org/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl#sha256=df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f
 # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0
 # pip numpy @ https://files.pythonhosted.org/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f
@@ -77,7 +77,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2
 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6
 # pip scipy @ https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7
-# pip tifffile @ https://files.pythonhosted.org/packages/5d/06/bd0a6097da704a7a7c34a94cfd771c3ea3c2f405dd214e790d22c93f6be1/tifffile-2025.5.10-py3-none-any.whl#sha256=e37147123c0542d67bc37ba5cdd67e12ea6fbe6e86c52bee037a9eb6a064e5ad
+# pip tifffile @ https://files.pythonhosted.org/packages/f5/34/59df6d47b6a8203e1db8f9a0585faf4cd68918e4a860f3bcb57909b6099f/tifffile-2025.5.24-py3-none-any.whl#sha256=2d913e41356425a2eeb4dc06dcc7286193cfa65ae1dedb5f51f04f60cf06713d
 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d
 # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7
 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2
diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
index 30d26d841954f..98aa76361cb56 100644
--- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
+++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
@@ -52,37 +52,37 @@ https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_2.conda#21f562
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 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833
-https://conda.anaconda.org/conda-forge/win-64/cython-3.1.0-py310h6bd2d47_1.conda#41d337072c52ae6b13bf362645cabd55
+https://conda.anaconda.org/conda-forge/win-64/cython-3.1.1-py310h6bd2d47_1.conda#165131d296d24f798fa76a26694d4565
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90
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 https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.7-py310hc19bc0b_0.conda#50d96539497fc7493cbe469fbb6b8b6e
-https://conda.anaconda.org/conda-forge/win-64/libclang13-20.1.5-default_h6e92b77_0.conda#2013532e0911dcc50ab4a2fd09d1d9a5
+https://conda.anaconda.org/conda-forge/win-64/libclang13-20.1.5-default_h6e92b77_1.conda#c5b6c1338035f155d15112a44d2de5f9
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-https://conda.anaconda.org/conda-forge/win-64/libglib-2.84.1-hbc94333_1.conda#e08d4b6e9a742d78e505b2d7038912bd
+https://conda.anaconda.org/conda-forge/win-64/libglib-2.84.2-hbc94333_0.conda#fee05801cc5db97bec20a5e78fb3905b
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-https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda#8e25221b702272394b86b0f4d7217f77
+https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162
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-https://conda.anaconda.org/conda-forge/noarch/setuptools-80.1.0-pyhff2d567_0.conda#f6f72d0837c79eaec77661be43e8a691
+https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c
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-https://conda.anaconda.org/conda-forge/win-64/tornado-6.5-py310ha8f682b_0.conda#84903de6733418c3b81e17fc942cd756
+https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.1-py310ha8f682b_0.conda#4c8f599990e386f3a0aba3f3bd8608da
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-https://conda.anaconda.org/conda-forge/win-64/coverage-7.8.0-py310h38315fa_0.conda#30a825dae940c63c55bca8df4f806f3e
+https://conda.anaconda.org/conda-forge/win-64/coverage-7.8.2-py310h38315fa_0.conda#5e09090744ab0b70b2882bc415c0d5ad
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-https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.0-pyhd8ed1ab_0.conda#3d7257f0a61c9aa4ffa3e324a887416b
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c
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diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock
index ba3443b69a90d..36f45c7e0dc7e 100644
--- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock
+++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock
@@ -45,7 +45,6 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.c
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxshmfence-1.3.3-hb9d3cd8_0.conda#9a809ce9f65460195777f2f2116bae02
 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553
-https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.0-h5888daf_0.conda#d6845ae4dea52a2f90178bf1829a21f8
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3
 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51
 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344155d33912347b37f0ae6c410a835
@@ -107,13 +106,13 @@ https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.7-py310h3788b33_0
 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e85703f0fd9594c81710dd5066471
 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3
 https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669
-https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.1-h3618099_1.conda#714c97d4ff495ab69d1fdfcadbcae985
+https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.2-h3618099_0.conda#072ab14a02164b7c0c089055368ff776
 https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda#c8013e438185f33b13814c5c488acd5c
 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a
 https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda#d172b34a443b95f86089e8229ddc9a17
 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-257.4-h4e0b6ca_1.conda#04bcf3055e51f8dde6fab9672fb9fca0
 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda#14dbe05b929e329dbaa6f2d0aa19466d
-https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda#8e25221b702272394b86b0f4d7217f77
+https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162
 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19
 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564
 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9
@@ -121,12 +120,12 @@ https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7d
 https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_3.conda#fd5062942bfa1b0bd5e0d2a4397b099e
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda#513d3c262ee49b54a8fec85c5bc99764
 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960
-https://conda.anaconda.org/conda-forge/noarch/setuptools-80.1.0-pyhff2d567_0.conda#f6f72d0837c79eaec77661be43e8a691
+https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c
 https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.1.0-pyh8a188c0_0.tar.bz2#a2995ee828f65687ac5b1e71a2ab1e0c
 https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215
-https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5-py310ha75aee5_0.conda#866b3582e300257f7e8dc67ec279a85b
+https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.1-py310ha75aee5_0.conda#6f3da1072c0c4d2a1beb1e84615f7c9c
 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.13.2-pyh29332c3_0.conda#83fc6ae00127671e301c9f44254c31b8
 https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-16.0.0-py310ha75aee5_0.conda#1d7a4b9202cdd10d56ecdd7f6c347190
 https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda#75cb7132eb58d97896e173ef12ac9986
@@ -136,20 +135,20 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.6-hb9d3cd8_0.co
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.1-hb9d3cd8_0.conda#4bdb303603e9821baf5fe5fdff1dc8f8
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda#96d57aba173e878a2089d5638016dc5e
 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a
-https://conda.anaconda.org/conda-forge/linux-64/coverage-7.8.0-py310h89163eb_0.conda#9f7865c17117d16f804b687b498e35fa
-https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d
+https://conda.anaconda.org/conda-forge/linux-64/coverage-7.8.2-py310h89163eb_0.conda#5ca8ab35287adc83b2d1996e5c2ac14c
+https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a
 https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.0-py310h89163eb_0.conda#4532df8a45ab4e37b2cc71186304dd5a
 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda#9ccd736d31e0c6e41f54e704e5312811
-https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.84.1-h4833e2c_1.conda#418de18c9b79a3d8583d90d27e0937c2
+https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.84.2-h4833e2c_0.conda#f2ec1facec64147850b7674633978050
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2#7583652522d71ad78ba536bba06940eb
 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda#2b7bb4f7562c8cf334fc2e20c2d28abc
 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869
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 https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.5-he9d0ab4_0.conda#8d2f5a2f019bd76ccba5eb771852d411
-https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.9.2-h65c71a3_0.conda#d045b1d878031eb497cab44e6392b1df
+https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a
 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.25-pthreads_h7a3da1a_0.conda#87661673941b5e702275fdf0fc095ad0
-https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.9-he970967_0.conda#ca2de8bbdc871bce41dbf59e51324165
+https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.10-he970967_0.conda#2e5bf4f1da39c0b32778561c3c4e5878
 https://conda.anaconda.org/conda-forge/linux-64/pillow-11.2.1-py310h7e6dc6c_0.conda#5645a243d90adb50909b9edc209d84fe
 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c
 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b
@@ -159,10 +158,10 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.6-hb9d3cd
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.6-hb9d3cd8_0.conda#b5fcc7172d22516e1f965490e65e33a4
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.6-hb9d3cd8_0.conda#5efa5fa6243a622445fdfd72aee15efa
 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee
-https://conda.anaconda.org/conda-forge/linux-64/glib-2.84.1-h6287aef_1.conda#35012688d30e1b52bff2ba5d1f342a50
+https://conda.anaconda.org/conda-forge/linux-64/glib-2.84.2-h6287aef_0.conda#704648df3a01d4d24bc2c0466b718d63
 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda#36d486d72ab64ffea932329a1d3729a3
-https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_0.conda#79a1be1cd92a7f2b62e6c0a7c2da8bf8
-https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_0.conda#9a912cce23df3fea9d2adb75e505b153
+https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_1.conda#330b1dadfa7c3205a01fa9599fabe808
+https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_1.conda#12117145218e7e1a528c8396ed803058
 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda#6fabc51f5e647d09cc010c40061557e0
 https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908
 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e
diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
index 3bfe3d34934ea..a3f0d92034d19 100644
--- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
+++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
@@ -49,7 +49,7 @@ https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c
 https://conda.anaconda.org/conda-forge/noarch/certifi-2025.4.26-pyhd8ed1ab_0.conda#c33eeaaa33f45031be34cda513df39b6
 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.2-pyhd8ed1ab_0.conda#40fe4284b8b5835a9073a645139f35af
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7
-https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.0-py310had8cdd9_1.conda#ad1ca59171c759e1b834eb6f199917ea
+https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.1-py310had8cdd9_1.conda#4904cb1ba6e72940ff22a5235554532d
 https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda#24c1ca34138ee57de72a943237cde4cc
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90
 https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda#0a802cb9888dd14eeefc611f05c40b6e
@@ -62,7 +62,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openbl
 https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669
 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a
 https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f
-https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda#8e25221b702272394b86b0f4d7217f77
+https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162
 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.29-pthreads_h6ec200e_0.conda#7e4d48870b3258bea920d51b7f495a81
 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564
 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9
@@ -72,9 +72,9 @@ https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda#461219d1a5bd61342293efa2c0c90eac
 https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda#88476ae6ebd24f39261e0854ac244f33
 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960
-https://conda.anaconda.org/conda-forge/noarch/setuptools-80.1.0-pyhff2d567_0.conda#f6f72d0837c79eaec77661be43e8a691
+https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c
 https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65
-https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e
+https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-3.0.1-pyhd8ed1ab_0.conda#755cf22df8693aa0d1aec1c123fa5863
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_1.conda#fa839b5ff59e192f411ccc7dae6588bb
 https://conda.anaconda.org/conda-forge/noarch/tabulate-0.9.0-pyhd8ed1ab_2.conda#959484a66b4b76befcddc4fa97c95567
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f
@@ -87,7 +87,7 @@ https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py310h8deb56e_0.cond
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a
 https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda#b4754fb1bdcb70c8fd54f918301582c6
 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda#446bd6c8cb26050d528881df495ce646
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.0-pyhd8ed1ab_0.conda#3d7257f0a61c9aa4ffa3e324a887416b
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c
 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-31_he106b2a_openblas.conda#abb32c727da370c481a1c206f5159ce9
 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda#452b98eafe050ecff932f0ec832dd03f
 https://conda.anaconda.org/conda-forge/linux-64/pillow-11.2.1-py310h7e6dc6c_0.conda#5645a243d90adb50909b9edc209d84fe
diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt
index 3111362d3d8d8..ab86e683f6f09 100644
--- a/build_tools/azure/ubuntu_atlas_lock.txt
+++ b/build_tools/azure/ubuntu_atlas_lock.txt
@@ -14,7 +14,7 @@ iniconfig==2.1.0
     # via pytest
 joblib==1.2.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
-meson==1.8.0
+meson==1.8.1
     # via meson-python
 meson-python==0.18.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock
index aa523781187bc..db2d896dc6ddc 100644
--- a/build_tools/circle/doc_linux-64_conda.lock
+++ b/build_tools/circle/doc_linux-64_conda.lock
@@ -50,7 +50,6 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.c
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553
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@@ -244,7 +243,7 @@ https://conda.anaconda.org/conda-forge/noarch/urllib3-2.4.0-pyhd8ed1ab_0.conda#c
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@@ -288,7 +287,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1
 # pip python-json-logger @ https://files.pythonhosted.org/packages/08/20/0f2523b9e50a8052bc6a8b732dfc8568abbdc42010aef03a2d750bdab3b2/python_json_logger-3.3.0-py3-none-any.whl#sha256=dd980fae8cffb24c13caf6e158d3d61c0d6d22342f932cb6e9deedab3d35eec7
 # pip pyyaml @ https://files.pythonhosted.org/packages/6b/4e/1523cb902fd98355e2e9ea5e5eb237cbc5f3ad5f3075fa65087aa0ecb669/PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ec031d5d2feb36d1d1a24380e4db6d43695f3748343d99434e6f5f9156aaa2ed
 # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9
-# pip rpds-py @ https://files.pythonhosted.org/packages/bf/03/d8a23a4610dc1ce7853bdb5c099de8050dae93cc8e7550ad6854073fbcb7/rpds_py-0.25.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c46bd76986e05689376d28fdc2b97d899576ce3e3aaa5a5f80f67a8300b26eb3
+# pip rpds-py @ https://files.pythonhosted.org/packages/eb/76/66b523ffc84cf47db56efe13ae7cf368dee2bacdec9d89b9baca5e2e6301/rpds_py-0.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0701942049095741a8aeb298a31b203e735d1c61f4423511d2b1a41dcd8a16da
 # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9
 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2
 # pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f
diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
index 719494825a991..cf6232d4ba950 100644
--- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
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+https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_1.conda#330b1dadfa7c3205a01fa9599fabe808
+https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_1.conda#12117145218e7e1a528c8396ed803058
 https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908
 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.5.0-py310h23f4a51_0.tar.bz2#9911225650b298776c8e8c083b5cacf1
diff --git a/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock b/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock
index 8484e580d3d0f..e4949a89892c4 100644
--- a/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock
+++ b/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock
@@ -39,7 +39,6 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.12-h86ecc28
 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.5-h57736b2_0.conda#25a5a7b797fe6e084e04ffe2db02fc62
 https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h68df207_7.conda#56398c28220513b9ea13d7b450acfb20
 https://conda.anaconda.org/conda-forge/linux-aarch64/double-conversion-3.3.1-h5ad3122_0.conda#399959d889e1a73fc99f12ce480e77e1
-https://conda.anaconda.org/conda-forge/linux-aarch64/expat-2.7.0-h5ad3122_0.conda#c22e14e241ade3d3a74c0409c3d582a2
 https://conda.anaconda.org/conda-forge/linux-aarch64/keyutils-1.6.1-h4e544f5_0.tar.bz2#1f24853e59c68892452ef94ddd8afd4b
 https://conda.anaconda.org/conda-forge/linux-aarch64/lerc-4.0.0-hfdc4d58_1.conda#60dceb7e876f4d74a9cbd42bbbc6b9cf
 https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.1.0-h86ecc28_2.conda#e64d0f3b59c7c4047446b97a8624a72d
@@ -83,7 +82,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h86ecc28_2.con
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7
 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833
 https://conda.anaconda.org/conda-forge/linux-aarch64/cyrus-sasl-2.1.27-hf6b2984_7.conda#7a85d417c8acd7a5215c082c5b9219e5
-https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.1.0-py310hc86cfe9_1.conda#3642551f371fe1a23b5ef5925294a11b
+https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.1.1-py310hc86cfe9_1.conda#927cfc32db9db46c1804d977aaa11173
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90
 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108
 https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.4.7-py310h5d7f10c_0.conda#b86d594bf17c9ad7a291593368ae8ba7
@@ -91,22 +90,22 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.17-hc88f144_0.conda
 https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-31_h1a9f1db_openblas.conda#48bd5bf15ccf3e409840be9caafc0ad5
 https://conda.anaconda.org/conda-forge/linux-aarch64/libcups-2.3.3-h405e4a8_4.conda#d42c670b0c96c1795fd859d5e0275a55
 https://conda.anaconda.org/conda-forge/linux-aarch64/libfreetype-2.13.3-h8af1aa0_1.conda#2d4a1c3dcabb80b4a56d5c34bdacea08
-https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.84.1-hc022ef1_1.conda#5bee669fdb0324a387626d59a193bce7
+https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.84.2-hc022ef1_0.conda#51323eab8e9f049d001424828c4c25a4
 https://conda.anaconda.org/conda-forge/linux-aarch64/libglx-1.7.0-hd24410f_2.conda#1d4269e233636148696a67e2d30dad2a
 https://conda.anaconda.org/conda-forge/linux-aarch64/libhiredis-1.0.2-h05efe27_0.tar.bz2#a87f068744fd20334cd41489eb163bee
 https://conda.anaconda.org/conda-forge/linux-aarch64/libxml2-2.13.8-he060846_0.conda#c73dfe6886cc8d39a09c357a36f91fb2
-https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda#8e25221b702272394b86b0f4d7217f77
+https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162
 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19
 https://conda.anaconda.org/conda-forge/linux-aarch64/openblas-0.3.29-pthreads_h3a8cbd8_0.conda#4ec5b6144709ced5e7933977675f61c6
 https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.3-h3f56577_0.conda#04231368e4af50d11184b50e14250993
 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9
 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda#513d3c262ee49b54a8fec85c5bc99764
-https://conda.anaconda.org/conda-forge/noarch/setuptools-80.1.0-pyhff2d567_0.conda#f6f72d0837c79eaec77661be43e8a691
+https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c
 https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215
-https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.5-py310h78583b1_0.conda#b50bfa58708978dcce6ccc8c468ff302
+https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.5.1-py310h78583b1_0.conda#e1e576b66cca7642b0a66310b675ea36
 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.13.2-pyh29332c3_0.conda#83fc6ae00127671e301c9f44254c31b8
 https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-16.0.0-py310ha766c32_0.conda#2936ce19a675e162962f396c7b40b905
 https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda#75cb7132eb58d97896e173ef12ac9986
@@ -116,18 +115,18 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxext-1.3.6-h57736b2
 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxfixes-6.0.1-h57736b2_0.conda#78f8715c002cc66991d7c11e3cf66039
 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.12-h86ecc28_0.conda#ae2c2dd0e2d38d249887727db2af960e
 https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.11.3-h4889ad1_0.conda#e0b9e519da2bf0fb8c48381daf87a194
-https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.13.6-h12b9eeb_3.tar.bz2#f3d63805602166bac09386741e00935e
+https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.16.2-heda779d_0.conda#9203b74bb1f3fa0d6f308094b3b44c1e
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a
 https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.58.0-py310heeae437_0.conda#426a52d57550926ebe1735ba0eacd99d
 https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.13.3-h8af1aa0_1.conda#71c4cbe1b384a8e7b56993394a435343
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.0-pyhd8ed1ab_0.conda#3d7257f0a61c9aa4ffa3e324a887416b
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c
 https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-31_hab92f65_openblas.conda#6b81dbae56a519f1ec2f25e0ee2f4334
 https://conda.anaconda.org/conda-forge/linux-aarch64/libgl-1.7.0-hd24410f_2.conda#0d00176464ebb25af83d40736a2cd3bb
 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-31_h411afd4_openblas.conda#41dbff5eb805a75c120a7b7a1c744dc2
 https://conda.anaconda.org/conda-forge/linux-aarch64/libllvm20-20.1.5-h07bd352_0.conda#d898466dd826e8acf6d0ee075028f6bd
-https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.9.2-hbab7b08_0.conda#7b47a2ccfb81b4be6be320b365e1cf33
+https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.10.0-hbab7b08_0.conda#36cd1db31e923c6068b7e0e6fce2cd7b
 https://conda.anaconda.org/conda-forge/linux-aarch64/libxslt-1.1.39-h1cc9640_0.conda#13e1d3f9188e85c6d59a98651aced002
-https://conda.anaconda.org/conda-forge/linux-aarch64/openldap-2.6.9-h30c48ee_0.conda#c07822a5de65ce9797b9afa257faa917
+https://conda.anaconda.org/conda-forge/linux-aarch64/openldap-2.6.10-h30c48ee_0.conda#48f31a61be512ec1929f4b4a9cedf4bd
 https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-11.2.1-py310h34c99de_0.conda#116816e9f034fcaeafcd878ef8b1e323
 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c
 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b
@@ -140,8 +139,8 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxi-1.8.2-h57736b2_0
 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrandr-1.5.4-h86ecc28_0.conda#dd3e74283a082381aa3860312e3c721e
 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxxf86vm-1.1.6-h86ecc28_0.conda#d745faa2d7c15092652e40a22bb261ed
 https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.15.0-h8dda3cd_1.conda#112b71b6af28b47c624bcbeefeea685b
-https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp20.1-20.1.5-default_h7d4303a_0.conda#832fb047ca0a4e4c83cb0f3004000f7b
-https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-20.1.5-default_h9e36cb9_0.conda#6072f3b5b573cfd6ad4562813dc0d4e7
+https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp20.1-20.1.5-default_h7d4303a_1.conda#e2c94afb8bc1364bc872a61cbd876688
+https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-20.1.5-default_h9e36cb9_1.conda#d98eeb2cba2804d5cffc7f17787211fc
 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-31_hc659ca5_openblas.conda#256bb281d78e5b8927ff13a1cde9f6f5
 https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-17.5-hf590da8_0.conda#b5a01e5aa04651ccf5865c2d029affa3
 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133

From de1525bd4b040da1d10921fe3a4bf534899ecd72 Mon Sep 17 00:00:00 2001
From: Christian Lorentzen 
Date: Tue, 27 May 2025 18:52:12 +0200
Subject: [PATCH 50/64] ENH avoid futile recomputation of R_sum in
 sparse_enet_coordinate_descent (#31387)

Co-authored-by: Omar Salman 
Co-authored-by: Omar Salman 
---
 .../sklearn.linear_model/31387.enhancement.rst   |  4 ++++
 sklearn/linear_model/_cd_fast.pyx                | 16 +++++++---------
 2 files changed, 11 insertions(+), 9 deletions(-)
 create mode 100644 doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst

diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst
new file mode 100644
index 0000000000000..98946414b872a
--- /dev/null
+++ b/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst
@@ -0,0 +1,4 @@
+- Fitting :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet` with
+  `fit_intercept=True` is a bit faster for sparse input `X` because an unnecessary
+  re-computation of the sum of residuals is avoided.
+  By :user:`Christian Lorentzen `
diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx
index 6f17622bc4789..ce598ebb011d2 100644
--- a/sklearn/linear_model/_cd_fast.pyx
+++ b/sklearn/linear_model/_cd_fast.pyx
@@ -400,6 +400,7 @@ def sparse_enet_coordinate_descent(
                 if center:
                     for jj in range(n_samples):
                         R[jj] += X_mean_ii * w_ii
+                        R_sum += R[jj]
             else:
                 # R = sw * (y - np.dot(X, w))
                 for jj in range(startptr, endptr):
@@ -412,9 +413,14 @@ def sparse_enet_coordinate_descent(
                     for jj in range(n_samples):
                         normalize_sum += sample_weight[jj] * X_mean_ii ** 2
                         R[jj] += sample_weight[jj] * X_mean_ii * w_ii
+                        R_sum += R[jj]
                 norm_cols_X[ii] = normalize_sum
             startptr = endptr
 
+        # Note: No need to update R_sum from here on because the update terms cancel
+        # each other: w_ii * np.sum(X[:,ii] - X_mean[ii]) = 0. R_sum is only ever
+        # needed and calculated if X_mean is provided.
+
         # tol *= np.dot(y, y)
         # with sample weights: tol *= y @ (sw * y)
         tol *= _dot(n_samples, &y[0], 1, &yw[0], 1)
@@ -460,9 +466,6 @@ def sparse_enet_coordinate_descent(
                     tmp += R[X_indices[jj]] * X_data[jj]
 
                 if center:
-                    R_sum = 0.0
-                    for jj in range(n_samples):
-                        R_sum += R[jj]
                     tmp -= R_sum * X_mean_ii
 
                 if positive and tmp < 0.0:
@@ -498,13 +501,8 @@ def sparse_enet_coordinate_descent(
                 # the tolerance: check the duality gap as ultimate stopping
                 # criterion
 
-                # sparse X.T / dense R dot product
-                if center:
-                    R_sum = 0.0
-                    for jj in range(n_samples):
-                        R_sum += R[jj]
-
                 # XtA = X.T @ R - beta * w
+                # sparse X.T / dense R dot product
                 for ii in range(n_features):
                     XtA[ii] = 0.0
                     for kk in range(X_indptr[ii], X_indptr[ii + 1]):

From f3b25213fa54c0fccadc897fe88b229f216e6a13 Mon Sep 17 00:00:00 2001
From: Olivier Grisel 
Date: Tue, 27 May 2025 19:04:15 +0200
Subject: [PATCH 51/64] FIX random failure in `test_solver_consistency` with
 SAG/SAGA solvers  (#31434)

---
 sklearn/linear_model/tests/test_ridge.py | 22 +++++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)

diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py
index 60b8a8bb3e144..24515195fb7cc 100644
--- a/sklearn/linear_model/tests/test_ridge.py
+++ b/sklearn/linear_model/tests/test_ridge.py
@@ -750,9 +750,8 @@ def _make_sparse_offset_regression(
     "n_samples,dtype,proportion_nonzero",
     [(20, "float32", 0.1), (40, "float32", 1.0), (20, "float64", 0.2)],
 )
-@pytest.mark.parametrize("seed", np.arange(3))
 def test_solver_consistency(
-    solver, proportion_nonzero, n_samples, dtype, sparse_container, seed
+    solver, proportion_nonzero, n_samples, dtype, sparse_container, global_random_seed
 ):
     alpha = 1.0
     noise = 50.0 if proportion_nonzero > 0.9 else 500.0
@@ -761,10 +760,9 @@ def test_solver_consistency(
         n_features=30,
         proportion_nonzero=proportion_nonzero,
         noise=noise,
-        random_state=seed,
+        random_state=global_random_seed,
         n_samples=n_samples,
     )
-
     # Manually scale the data to avoid pathological cases. We use
     # minmax_scale to deal with the sparse case without breaking
     # the sparsity pattern.
@@ -778,7 +776,21 @@ def test_solver_consistency(
     if solver == "ridgecv":
         ridge = RidgeCV(alphas=[alpha])
     else:
-        ridge = Ridge(solver=solver, tol=1e-10, alpha=alpha)
+        if solver.startswith("sag"):
+            # Avoid ConvergenceWarning for sag and saga solvers.
+            tol = 1e-7
+            max_iter = 100_000
+        else:
+            tol = 1e-10
+            max_iter = None
+
+        ridge = Ridge(
+            alpha=alpha,
+            solver=solver,
+            max_iter=max_iter,
+            tol=tol,
+            random_state=global_random_seed,
+        )
     ridge.fit(X, y)
     assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3)
     assert_allclose(ridge.intercept_, svd_ridge.intercept_, atol=1e-3, rtol=1e-3)

From 121d62ebd81ca63e051fcac089ebe10fa9417375 Mon Sep 17 00:00:00 2001
From: Christian Lorentzen 
Date: Wed, 28 May 2025 08:43:39 +0200
Subject: [PATCH 52/64] DOC tiny whatsnew update for PR #31387 (#31437)

---
 .../upcoming_changes/sklearn.linear_model/31387.enhancement.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst
index 98946414b872a..8b8751347b843 100644
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst
+++ b/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst
@@ -1,4 +1,4 @@
 - Fitting :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet` with
-  `fit_intercept=True` is a bit faster for sparse input `X` because an unnecessary
+  `fit_intercept=True` is faster for sparse input `X` because an unnecessary
   re-computation of the sum of residuals is avoided.
   By :user:`Christian Lorentzen `

From 8f5f02b9b6135cfac97cac5fcaae79890faac2b5 Mon Sep 17 00:00:00 2001
From: $id 
Date: Wed, 28 May 2025 13:21:26 +0530
Subject: [PATCH 53/64] DOC Add link to spectral coclustering (#31422)

Co-authored-by: Cloponaclock1 
---
 sklearn/cluster/_bicluster.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/sklearn/cluster/_bicluster.py b/sklearn/cluster/_bicluster.py
index e7ffc72870dca..04a4e68024d33 100644
--- a/sklearn/cluster/_bicluster.py
+++ b/sklearn/cluster/_bicluster.py
@@ -307,6 +307,9 @@ class SpectralCoclustering(BaseSpectral):
     array([0, 0], dtype=int32)
     >>> clustering
     SpectralCoclustering(n_clusters=2, random_state=0)
+
+    For a more detailed example, see the following:
+    :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py`.
     """
 
     _parameter_constraints: dict = {

From 37839c10a84d11b02b293639122f3cbbd56a74c4 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre 
Date: Wed, 28 May 2025 14:17:19 +0200
Subject: [PATCH 54/64] DOC: use curve_kwargs instead of kwargs in example to
 avoid warnings (#31447)

---
 examples/miscellaneous/plot_outlier_detection_bench.py   | 9 +++------
 .../miscellaneous/plot_roc_curve_visualization_api.py    | 6 ++++--
 examples/model_selection/plot_cost_sensitive_learning.py | 3 +--
 examples/model_selection/plot_det.py                     | 7 ++++++-
 examples/model_selection/plot_roc.py                     | 6 +++---
 examples/model_selection/plot_roc_crossval.py            | 3 +--
 6 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/examples/miscellaneous/plot_outlier_detection_bench.py b/examples/miscellaneous/plot_outlier_detection_bench.py
index 600eceb1a06b3..933902500ef8b 100644
--- a/examples/miscellaneous/plot_outlier_detection_bench.py
+++ b/examples/miscellaneous/plot_outlier_detection_bench.py
@@ -355,8 +355,7 @@ def fit_predict(estimator, X):
         ax=ax,
         plot_chance_level=(model_idx == len(n_neighbors_list) - 1),
         chance_level_kw={"linestyle": (0, (1, 10))},
-        linestyle=linestyle,
-        linewidth=2,
+        curve_kwargs=dict(linestyle=linestyle, linewidth=2),
     )
 _ = ax.set_title("RobustScaler with varying n_neighbors\non forestcover dataset")
 
@@ -395,8 +394,7 @@ def fit_predict(estimator, X):
         ax=ax,
         plot_chance_level=(model_idx == len(preprocessor_list) - 1),
         chance_level_kw={"linestyle": (0, (1, 10))},
-        linestyle=linestyle,
-        linewidth=2,
+        curve_kwargs=dict(linestyle=linestyle, linewidth=2),
     )
 _ = ax.set_title("Fixed n_neighbors with varying preprocessing\non forestcover dataset")
 
@@ -447,8 +445,7 @@ def fit_predict(estimator, X):
         ax=ax,
         plot_chance_level=(model_idx == len(preprocessor_list) - 1),
         chance_level_kw={"linestyle": (0, (1, 10))},
-        linestyle=linestyle,
-        linewidth=2,
+        curve_kwargs=dict(linestyle=linestyle, linewidth=2),
     )
 ax.set_title(
     "Fixed n_neighbors with varying preprocessing\non cardiotocography dataset"
diff --git a/examples/miscellaneous/plot_roc_curve_visualization_api.py b/examples/miscellaneous/plot_roc_curve_visualization_api.py
index d377d321e061e..1aacbd9de3631 100644
--- a/examples/miscellaneous/plot_roc_curve_visualization_api.py
+++ b/examples/miscellaneous/plot_roc_curve_visualization_api.py
@@ -54,6 +54,8 @@
 rfc = RandomForestClassifier(n_estimators=10, random_state=42)
 rfc.fit(X_train, y_train)
 ax = plt.gca()
-rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
-svc_disp.plot(ax=ax, alpha=0.8)
+rfc_disp = RocCurveDisplay.from_estimator(
+    rfc, X_test, y_test, ax=ax, curve_kwargs=dict(alpha=0.8)
+)
+svc_disp.plot(ax=ax, curve_kwargs=dict(alpha=0.8))
 plt.show()
diff --git a/examples/model_selection/plot_cost_sensitive_learning.py b/examples/model_selection/plot_cost_sensitive_learning.py
index 9845d27661374..6b5b651463b05 100644
--- a/examples/model_selection/plot_cost_sensitive_learning.py
+++ b/examples/model_selection/plot_cost_sensitive_learning.py
@@ -321,8 +321,7 @@ def plot_roc_pr_curves(vanilla_model, tuned_model, *, title):
             X_test,
             y_test,
             pos_label=pos_label,
-            linestyle=linestyle,
-            color=color,
+            curve_kwargs=dict(linestyle=linestyle, color=color),
             ax=axs[1],
             name=name,
             plot_chance_level=idx == 1,
diff --git a/examples/model_selection/plot_det.py b/examples/model_selection/plot_det.py
index 873d00d696d95..4a22cdcd44eb8 100644
--- a/examples/model_selection/plot_det.py
+++ b/examples/model_selection/plot_det.py
@@ -103,7 +103,12 @@
     )
     clf.fit(X_train, y_train)
     RocCurveDisplay.from_estimator(
-        clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle
+        clf,
+        X_test,
+        y_test,
+        ax=ax_roc,
+        name=name,
+        curve_kwargs=dict(color=color, linestyle=linestyle),
     )
     DetCurveDisplay.from_estimator(
         clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle
diff --git a/examples/model_selection/plot_roc.py b/examples/model_selection/plot_roc.py
index a482ad5f4ab95..9e659b9a2aa64 100644
--- a/examples/model_selection/plot_roc.py
+++ b/examples/model_selection/plot_roc.py
@@ -129,7 +129,7 @@
     y_onehot_test[:, class_id],
     y_score[:, class_id],
     name=f"{class_of_interest} vs the rest",
-    color="darkorange",
+    curve_kwargs=dict(color="darkorange"),
     plot_chance_level=True,
     despine=True,
 )
@@ -165,7 +165,7 @@
     y_onehot_test.ravel(),
     y_score.ravel(),
     name="micro-average OvR",
-    color="darkorange",
+    curve_kwargs=dict(color="darkorange"),
     plot_chance_level=True,
     despine=True,
 )
@@ -290,7 +290,7 @@
         y_onehot_test[:, class_id],
         y_score[:, class_id],
         name=f"ROC curve for {target_names[class_id]}",
-        color=color,
+        curve_kwargs=dict(color=color),
         ax=ax,
         plot_chance_level=(class_id == 2),
         despine=True,
diff --git a/examples/model_selection/plot_roc_crossval.py b/examples/model_selection/plot_roc_crossval.py
index fb6432a71ed79..868454626451c 100644
--- a/examples/model_selection/plot_roc_crossval.py
+++ b/examples/model_selection/plot_roc_crossval.py
@@ -89,8 +89,7 @@
         X[test],
         y[test],
         name=f"ROC fold {fold}",
-        alpha=0.3,
-        lw=1,
+        curve_kwargs=dict(alpha=0.3, lw=1),
         ax=ax,
         plot_chance_level=(fold == n_splits - 1),
     )

From 7fda08ad017d0cc5f871a2418871e7e792bdb4b6 Mon Sep 17 00:00:00 2001
From: Lucy Liu 
Date: Wed, 28 May 2025 23:10:02 +1000
Subject: [PATCH 55/64] TST Fix array API `test_fill_or_add_to_diagonal`
 (#31439)

Co-authored-by: Olivier Grisel 
---
 sklearn/utils/tests/test_array_api.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py
index c0322d013b90d..4dfbfd4d62ea1 100644
--- a/sklearn/utils/tests/test_array_api.py
+++ b/sklearn/utils/tests/test_array_api.py
@@ -581,10 +581,14 @@ def test_count_nonzero(
 @pytest.mark.parametrize("wrap", [True, False])
 def test_fill_or_add_to_diagonal(array_namespace, device_, dtype_name, wrap):
     xp = _array_api_for_tests(array_namespace, device_)
-    array_np = numpy.zeros((5, 4), dtype=numpy.int64)
-    array_xp = xp.asarray(array_np)
-    _fill_or_add_to_diagonal(array_xp, value=1, xp=xp, add_value=False, wrap=wrap)
+
+    array_np = numpy.zeros((5, 4), dtype=dtype_name)
+    array_xp = xp.asarray(array_np.copy(), device=device_)
+
     numpy.fill_diagonal(array_np, val=1, wrap=wrap)
+    with config_context(array_api_dispatch=True):
+        _fill_or_add_to_diagonal(array_xp, value=1, xp=xp, add_value=False, wrap=wrap)
+
     assert_array_equal(_convert_to_numpy(array_xp, xp=xp), array_np)
 
 

From 5b5531c0cfb402e7c8db020c54bfc16fbbf18c3f Mon Sep 17 00:00:00 2001
From: "Christine P. Chai" 
Date: Wed, 28 May 2025 19:15:39 -0700
Subject: [PATCH 56/64] DOC: Update link to Least Angle Regression paper
 (#31433)

---
 doc/modules/linear_model.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
index b575e4597b6fa..9edd90321bd02 100644
--- a/doc/modules/linear_model.rst
+++ b/doc/modules/linear_model.rst
@@ -654,7 +654,7 @@ or :func:`lars_path_gram`.
   .. rubric:: References
 
   * Original Algorithm is detailed in the paper `Least Angle Regression
-    `_
+    `_
     by Hastie et al.
 
 .. _omp:

From 73e2473a1dd7f51047009b5f4fe4d2634f4b0217 Mon Sep 17 00:00:00 2001
From: Lucy Liu 
Date: Thu, 29 May 2025 18:21:57 +1000
Subject: [PATCH 57/64] MNT Make sample_weight checking more consistent in
 regression metrics (#30886)

---
 .../sklearn.metrics/30886.fix.rst             | 17 +++++
 sklearn/metrics/_regression.py                | 67 +++++++++----------
 sklearn/metrics/tests/test_common.py          | 26 +++++++
 sklearn/metrics/tests/test_regression.py      |  8 ++-
 sklearn/utils/validation.py                   | 12 ++--
 5 files changed, 87 insertions(+), 43 deletions(-)
 create mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst

diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst
new file mode 100644
index 0000000000000..ec0418b290040
--- /dev/null
+++ b/doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst
@@ -0,0 +1,17 @@
+- Additional `sample_weight` checking has been added to
+  :func:`metrics.mean_absolute_error`,
+  :func:`metrics.mean_pinball_loss`,
+  :func:`metrics.mean_absolute_percentage_error`,
+  :func:`metrics.mean_squared_error`,
+  :func:`metrics.root_mean_squared_error`,
+  :func:`metrics.mean_squared_log_error`,
+  :func:`metrics.root_mean_squared_log_error`,
+  :func:`metrics.explained_variance_score`,
+  :func:`metrics.r2_score`,
+  :func:`metrics.mean_tweedie_deviance`,
+  :func:`metrics.mean_poisson_deviance`,
+  :func:`metrics.mean_gamma_deviance` and
+  :func:`metrics.d2_tweedie_score`.
+  `sample_weight` can only be 1D, consistent to `y_true` and `y_pred` in length
+  or a scalar.
+  By :user:`Lucy Liu `.
diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py
index 4c46346d63d92..0731e00ce3a1a 100644
--- a/sklearn/metrics/_regression.py
+++ b/sklearn/metrics/_regression.py
@@ -57,8 +57,10 @@
 ]
 
 
-def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric", xp=None):
-    """Check that y_true and y_pred belong to the same regression task.
+def _check_reg_targets(
+    y_true, y_pred, sample_weight, multioutput, dtype="numeric", xp=None
+):
+    """Check that y_true, y_pred and sample_weight belong to the same regression task.
 
     To reduce redundancy when calling `_find_matching_floating_dtype`,
     please use `_check_reg_targets_with_floating_dtype` instead.
@@ -71,6 +73,9 @@ def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric", xp=None):
     y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
         Estimated target values.
 
+    sample_weight : array-like of shape (n_samples,) or None
+        Sample weights.
+
     multioutput : array-like or string in ['raw_values', uniform_average',
         'variance_weighted'] or None
         None is accepted due to backward compatibility of r2_score().
@@ -95,6 +100,9 @@ def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric", xp=None):
     y_pred : array-like of shape (n_samples, n_outputs)
         Estimated target values.
 
+    sample_weight : array-like of shape (n_samples,) or None
+        Sample weights.
+
     multioutput : array-like of shape (n_outputs) or string in ['raw_values',
         uniform_average', 'variance_weighted'] or None
         Custom output weights if ``multioutput`` is array-like or
@@ -103,9 +111,11 @@ def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric", xp=None):
     """
     xp, _ = get_namespace(y_true, y_pred, multioutput, xp=xp)
 
-    check_consistent_length(y_true, y_pred)
+    check_consistent_length(y_true, y_pred, sample_weight)
     y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
     y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
+    if sample_weight is not None:
+        sample_weight = _check_sample_weight(sample_weight, y_true, dtype=dtype)
 
     if y_true.ndim == 1:
         y_true = xp.reshape(y_true, (-1, 1))
@@ -141,14 +151,13 @@ def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric", xp=None):
             )
     y_type = "continuous" if n_outputs == 1 else "continuous-multioutput"
 
-    return y_type, y_true, y_pred, multioutput
+    return y_type, y_true, y_pred, sample_weight, multioutput
 
 
 def _check_reg_targets_with_floating_dtype(
     y_true, y_pred, sample_weight, multioutput, xp=None
 ):
-    """Ensures that y_true, y_pred, and sample_weight correspond to the same
-    regression task.
+    """Ensures y_true, y_pred, and sample_weight correspond to same regression task.
 
     Extends `_check_reg_targets` by automatically selecting a suitable floating-point
     data type for inputs using `_find_matching_floating_dtype`.
@@ -197,15 +206,10 @@ def _check_reg_targets_with_floating_dtype(
     """
     dtype_name = _find_matching_floating_dtype(y_true, y_pred, sample_weight, xp=xp)
 
-    y_type, y_true, y_pred, multioutput = _check_reg_targets(
-        y_true, y_pred, multioutput, dtype=dtype_name, xp=xp
+    y_type, y_true, y_pred, sample_weight, multioutput = _check_reg_targets(
+        y_true, y_pred, sample_weight, multioutput, dtype=dtype_name, xp=xp
     )
 
-    # _check_reg_targets does not accept sample_weight as input.
-    # Convert sample_weight's data type separately to match dtype_name.
-    if sample_weight is not None:
-        sample_weight = xp.asarray(sample_weight, dtype=dtype_name)
-
     return y_type, y_true, y_pred, sample_weight, multioutput
 
 
@@ -282,8 +286,6 @@ def mean_absolute_error(
         )
     )
 
-    check_consistent_length(y_true, y_pred, sample_weight)
-
     output_errors = _average(
         xp.abs(y_pred - y_true), weights=sample_weight, axis=0, xp=xp
     )
@@ -383,7 +385,6 @@ def mean_pinball_loss(
         )
     )
 
-    check_consistent_length(y_true, y_pred, sample_weight)
     diff = y_true - y_pred
     sign = xp.astype(diff >= 0, diff.dtype)
     loss = alpha * sign * diff - (1 - alpha) * (1 - sign) * diff
@@ -489,7 +490,6 @@ def mean_absolute_percentage_error(
             y_true, y_pred, sample_weight, multioutput, xp=xp
         )
     )
-    check_consistent_length(y_true, y_pred, sample_weight)
     epsilon = xp.asarray(xp.finfo(xp.float64).eps, dtype=y_true.dtype, device=device_)
     y_true_abs = xp.abs(y_true)
     mape = xp.abs(y_pred - y_true) / xp.maximum(y_true_abs, epsilon)
@@ -581,7 +581,6 @@ def mean_squared_error(
             y_true, y_pred, sample_weight, multioutput, xp=xp
         )
     )
-    check_consistent_length(y_true, y_pred, sample_weight)
     output_errors = _average((y_true - y_pred) ** 2, axis=0, weights=sample_weight)
 
     if isinstance(multioutput, str):
@@ -753,8 +752,10 @@ def mean_squared_log_error(
     """
     xp, _ = get_namespace(y_true, y_pred)
 
-    _, y_true, y_pred, _, _ = _check_reg_targets_with_floating_dtype(
-        y_true, y_pred, sample_weight, multioutput, xp=xp
+    _, y_true, y_pred, sample_weight, multioutput = (
+        _check_reg_targets_with_floating_dtype(
+            y_true, y_pred, sample_weight, multioutput, xp=xp
+        )
     )
 
     if xp.any(y_true <= -1) or xp.any(y_pred <= -1):
@@ -829,8 +830,10 @@ def root_mean_squared_log_error(
     """
     xp, _ = get_namespace(y_true, y_pred)
 
-    _, y_true, y_pred, _, _ = _check_reg_targets_with_floating_dtype(
-        y_true, y_pred, sample_weight, multioutput, xp=xp
+    _, y_true, y_pred, sample_weight, multioutput = (
+        _check_reg_targets_with_floating_dtype(
+            y_true, y_pred, sample_weight, multioutput, xp=xp
+        )
     )
 
     if xp.any(y_true <= -1) or xp.any(y_pred <= -1):
@@ -912,13 +915,12 @@ def median_absolute_error(
     >>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
     0.85
     """
-    y_type, y_true, y_pred, multioutput = _check_reg_targets(
-        y_true, y_pred, multioutput
+    _, y_true, y_pred, sample_weight, multioutput = _check_reg_targets(
+        y_true, y_pred, sample_weight, multioutput
     )
     if sample_weight is None:
         output_errors = np.median(np.abs(y_pred - y_true), axis=0)
     else:
-        sample_weight = _check_sample_weight(sample_weight, y_pred)
         output_errors = _weighted_percentile(
             np.abs(y_pred - y_true), sample_weight=sample_weight
         )
@@ -1106,8 +1108,6 @@ def explained_variance_score(
         )
     )
 
-    check_consistent_length(y_true, y_pred, sample_weight)
-
     y_diff_avg = _average(y_true - y_pred, weights=sample_weight, axis=0)
     numerator = _average(
         (y_true - y_pred - y_diff_avg) ** 2, weights=sample_weight, axis=0
@@ -1278,8 +1278,6 @@ def r2_score(
         )
     )
 
-    check_consistent_length(y_true, y_pred, sample_weight)
-
     if _num_samples(y_pred) < 2:
         msg = "R^2 score is not well-defined with less than two samples."
         warnings.warn(msg, UndefinedMetricWarning)
@@ -1343,7 +1341,9 @@ def max_error(y_true, y_pred):
     1.0
     """
     xp, _ = get_namespace(y_true, y_pred)
-    y_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred, None, xp=xp)
+    y_type, y_true, y_pred, _, _ = _check_reg_targets(
+        y_true, y_pred, sample_weight=None, multioutput=None, xp=xp
+    )
     if y_type == "continuous-multioutput":
         raise ValueError("Multioutput not supported in max_error")
     return float(xp.max(xp.abs(y_true - y_pred)))
@@ -1448,7 +1448,6 @@ def mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0):
     )
     if y_type == "continuous-multioutput":
         raise ValueError("Multioutput not supported in mean_tweedie_deviance")
-    check_consistent_length(y_true, y_pred, sample_weight)
 
     if sample_weight is not None:
         sample_weight = column_or_1d(sample_weight)
@@ -1773,10 +1772,9 @@ def d2_pinball_score(
     >>> d2_pinball_score(y_true, y_true, alpha=0.1)
     1.0
     """
-    y_type, y_true, y_pred, multioutput = _check_reg_targets(
-        y_true, y_pred, multioutput
+    _, y_true, y_pred, sample_weight, multioutput = _check_reg_targets(
+        y_true, y_pred, sample_weight, multioutput
     )
-    check_consistent_length(y_true, y_pred, sample_weight)
 
     if _num_samples(y_pred) < 2:
         msg = "D^2 score is not well-defined with less than two samples."
@@ -1796,7 +1794,6 @@ def d2_pinball_score(
             np.percentile(y_true, q=alpha * 100, axis=0), (len(y_true), 1)
         )
     else:
-        sample_weight = _check_sample_weight(sample_weight, y_true)
         y_quantile = np.tile(
             _weighted_percentile(
                 y_true, sample_weight=sample_weight, percentile_rank=alpha * 100
diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
index 00e47f04b5b57..bad71e29573b8 100644
--- a/sklearn/metrics/tests/test_common.py
+++ b/sklearn/metrics/tests/test_common.py
@@ -1588,6 +1588,32 @@ def test_regression_sample_weight_invariance(name):
     check_sample_weight_invariance(name, metric, y_true, y_pred)
 
 
+@pytest.mark.parametrize(
+    "name",
+    sorted(
+        set(ALL_METRICS).intersection(set(REGRESSION_METRICS))
+        - METRICS_WITHOUT_SAMPLE_WEIGHT
+    ),
+)
+def test_regression_with_invalid_sample_weight(name):
+    # Check that `sample_weight` with incorrect length raises error
+    n_samples = 50
+    random_state = check_random_state(0)
+    y_true = random_state.random_sample(size=(n_samples,))
+    y_pred = random_state.random_sample(size=(n_samples,))
+    metric = ALL_METRICS[name]
+
+    sample_weight = random_state.random_sample(size=(n_samples - 1,))
+    with pytest.raises(ValueError, match="Found input variables with inconsistent"):
+        metric(y_true, y_pred, sample_weight=sample_weight)
+
+    sample_weight = random_state.random_sample(size=(n_samples * 2,)).reshape(
+        (n_samples, 2)
+    )
+    with pytest.raises(ValueError, match="Sample weights must be 1D array or scalar"):
+        metric(y_true, y_pred, sample_weight=sample_weight)
+
+
 @pytest.mark.parametrize(
     "name",
     sorted(
diff --git a/sklearn/metrics/tests/test_regression.py b/sklearn/metrics/tests/test_regression.py
index 5e90727583189..396ae5d0ffae1 100644
--- a/sklearn/metrics/tests/test_regression.py
+++ b/sklearn/metrics/tests/test_regression.py
@@ -330,7 +330,9 @@ def test__check_reg_targets():
 
     for (type1, y1, n_out1), (type2, y2, n_out2) in product(EXAMPLES, repeat=2):
         if type1 == type2 and n_out1 == n_out2:
-            y_type, y_check1, y_check2, multioutput = _check_reg_targets(y1, y2, None)
+            y_type, y_check1, y_check2, _, _ = _check_reg_targets(
+                y1, y2, sample_weight=None, multioutput=None
+            )
             assert type1 == y_type
             if type1 == "continuous":
                 assert_array_equal(y_check1, np.reshape(y1, (-1, 1)))
@@ -340,7 +342,7 @@ def test__check_reg_targets():
                 assert_array_equal(y_check2, y2)
         else:
             with pytest.raises(ValueError):
-                _check_reg_targets(y1, y2, None)
+                _check_reg_targets(y1, y2, sample_weight=None, multioutput=None)
 
 
 def test__check_reg_targets_exception():
@@ -351,7 +353,7 @@ def test__check_reg_targets_exception():
         )
     )
     with pytest.raises(ValueError, match=expected_message):
-        _check_reg_targets([1, 2, 3], [[1], [2], [3]], invalid_multioutput)
+        _check_reg_targets([1, 2, 3], [[1], [2], [3]], None, invalid_multioutput)
 
 
 def test_regression_multioutput_array():
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index 324827323168a..86bdd07c41f1c 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -2169,16 +2169,18 @@ def _check_sample_weight(
     """
     n_samples = _num_samples(X)
 
-    if dtype is not None and dtype not in [np.float32, np.float64]:
-        dtype = np.float64
+    xp, _ = get_namespace(X)
+
+    if dtype is not None and dtype not in [xp.float32, xp.float64]:
+        dtype = xp.float64
 
     if sample_weight is None:
-        sample_weight = np.ones(n_samples, dtype=dtype)
+        sample_weight = xp.ones(n_samples, dtype=dtype)
     elif isinstance(sample_weight, numbers.Number):
-        sample_weight = np.full(n_samples, sample_weight, dtype=dtype)
+        sample_weight = xp.full(n_samples, sample_weight, dtype=dtype)
     else:
         if dtype is None:
-            dtype = [np.float64, np.float32]
+            dtype = [xp.float64, xp.float32]
         sample_weight = check_array(
             sample_weight,
             accept_sparse=False,

From f4812380c9af8b091d50ab4fa7730b41df25d377 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?=
 
Date: Fri, 30 May 2025 15:22:40 +0200
Subject: [PATCH 58/64] set version

---
 sklearn/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/__init__.py b/sklearn/__init__.py
index 3c62c52dbf130..1ee488e44d713 100644
--- a/sklearn/__init__.py
+++ b/sklearn/__init__.py
@@ -42,7 +42,7 @@
 # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 #
-__version__ = "1.7.0rc1"
+__version__ = "1.7.0"
 
 
 # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded

From 0fac632e43c6084a86fd58262195aaf3cb09c271 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?=
 
Date: Fri, 30 May 2025 15:31:34 +0200
Subject: [PATCH 59/64] generate changelog

---
 .../array-api/29519.feature.rst               |   3 -
 .../array-api/29978.feature.rst               |   3 -
 .../array-api/30340.other.rst                 |   4 -
 .../array-api/30395.feature.rst               |   4 -
 .../array-api/30819.feature.rst               |   2 -
 .../array-api/30838.feature.rst               |   2 -
 .../array-api/31190.feature.rst               |   2 -
 .../array-api/31204.feature.rst               |   2 -
 .../changed-models/31316.fix.rst              |   5 -
 .../many-modules/30858.other.rst              |   7 --
 .../metadata-routing/30833.feature.rst        |   4 -
 .../sklearn.base/30763.enhancement.rst        |   4 -
 .../sklearn.calibration/30873.fix.rst         |   7 --
 .../sklearn.compose/31167.api.rst             |   4 -
 .../sklearn.covariance/30483.fix.rst          |   2 -
 .../sklearn.datasets/30196.enhancement.rst    |   3 -
 .../sklearn.decomposition/30443.feature.rst   |   4 -
 .../sklearn.ensemble/27124.feature.rst        |   6 --
 .../sklearn.ensemble/30649.fix.rst            |   2 -
 .../30179.enhancement.rst                     |   3 -
 .../sklearn.feature_selection/31107.fix.rst   |   4 -
 .../22227.enhancement.rst                     |   1 -
 .../sklearn.inspection/26202.enhancement.rst  |   5 -
 .../sklearn.inspection/29797.enhancement.rst  |   4 -
 .../sklearn.inspection/30409.api.rst          |   5 -
 .../sklearn.inspection/31146.fix.rst          |   4 -
 .../sklearn.linear_model/30057.fix.rst        |   5 -
 .../sklearn.linear_model/30521.fix.rst        |   4 -
 .../sklearn.linear_model/30616.api.rst        |   9 --
 .../sklearn.linear_model/30644.fix.rst        |   3 -
 .../30730.enhancement.rst                     |   3 -
 .../sklearn.linear_model/31094.fix.rst        |   3 -
 .../sklearn.linear_model/31241.api.rst        |   7 --
 .../31387.enhancement.rst                     |   4 -
 .../sklearn.manifold/30514.fix.rst            |   4 -
 .../sklearn.manifold/31117.enhancement.rst    |   3 -
 .../sklearn.manifold/31117.fix.rst            |   5 -
 .../sklearn.metrics/22046.feature.rst         |   6 --
 .../sklearn.metrics/22046.fix.rst             |   3 -
 .../sklearn.metrics/28981.api.rst             |   3 -
 .../sklearn.metrics/29151.enhancement.rst     |   6 --
 .../sklearn.metrics/29151.fix.rst             |   4 -
 .../sklearn.metrics/29288.api.rst             |   4 -
 .../sklearn.metrics/29288.enhancement.rst     |   4 -
 .../sklearn.metrics/29288.fix.rst             |   3 -
 .../sklearn.metrics/29727.fix.rst             |   3 -
 .../sklearn.metrics/29865.api.rst             |   4 -
 .../sklearn.metrics/30399.feature.rst         |   4 -
 .../sklearn.metrics/30886.fix.rst             |  17 ---
 .../sklearn.metrics/30903.fix.rst             |   3 -
 .../sklearn.metrics/31065.fix.rst             |   3 -
 .../sklearn.mixture/28559.feature.rst         |   5 -
 .../sklearn.mixture/30414.efficiency.rst      |   4 -
 .../sklearn.mixture/30415.efficiency.rst      |   5 -
 .../sklearn.model_selection/30743.fix.rst     |   3 -
 .../sklearn.multiclass/31228.fix.rst          |   5 -
 .../sklearn.multioutput/30152.enhancement.rst |   3 -
 .../sklearn.neural_network/24788.fix.rst      |   3 -
 .../sklearn.neural_network/30155.feature.rst  |   3 -
 .../sklearn.neural_network/30712.feature.rst  |   3 -
 .../sklearn.pipeline/30406.enhancement.rst    |   4 -
 .../29907.enhancement.rst                     |   6 --
 .../sklearn.preprocessing/29907.fix.rst       |   6 --
 .../sklearn.preprocessing/31227.fix.rst       |   6 --
 .../sklearn.svm/30057.fix.rst                 |   4 -
 .../sklearn.utils/26335.enhancement.rst       |   4 -
 .../sklearn.utils/29907.enhancement.rst       |   4 -
 .../sklearn.utils/30057.enhancement.rst       |   3 -
 .../sklearn.utils/30380.enhancement.rst       |   2 -
 .../sklearn.utils/30775.fix.rst               |   5 -
 .../sklearn.utils/30819.fix.rst               |   4 -
 .../sklearn.utils/31040.enhancement.rst       |   4 -
 doc/whats_new/v1.7.rst                        | 100 ++++++++++++++----
 73 files changed, 82 insertions(+), 317 deletions(-)
 delete mode 100644 doc/whats_new/upcoming_changes/array-api/29519.feature.rst
 delete mode 100644 doc/whats_new/upcoming_changes/array-api/29978.feature.rst
 delete mode 100644 doc/whats_new/upcoming_changes/array-api/30340.other.rst
 delete mode 100644 doc/whats_new/upcoming_changes/array-api/30395.feature.rst
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 delete mode 100644 doc/whats_new/upcoming_changes/array-api/31204.feature.rst
 delete mode 100644 doc/whats_new/upcoming_changes/changed-models/31316.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/many-modules/30858.other.rst
 delete mode 100644 doc/whats_new/upcoming_changes/metadata-routing/30833.feature.rst
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 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.manifold/31117.fix.rst
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 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/29151.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/29288.api.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/29288.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/29288.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/29727.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/29865.api.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/30903.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.metrics/31065.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.mixture/28559.feature.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.mixture/30414.efficiency.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.mixture/30415.efficiency.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.model_selection/30743.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.multiclass/31228.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.multioutput/30152.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.neural_network/24788.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.neural_network/30155.feature.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.neural_network/30712.feature.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.pipeline/30406.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.preprocessing/31227.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.svm/30057.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/26335.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/29907.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/30057.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/30380.enhancement.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/30775.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/30819.fix.rst
 delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/31040.enhancement.rst

diff --git a/doc/whats_new/upcoming_changes/array-api/29519.feature.rst b/doc/whats_new/upcoming_changes/array-api/29519.feature.rst
deleted file mode 100644
index 19f800ee45b4b..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/29519.feature.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :func:`sklearn.utils.check_consistent_length` now supports Array API compatible
-  inputs.
-  By :user:`Stefanie Senger `
diff --git a/doc/whats_new/upcoming_changes/array-api/29978.feature.rst b/doc/whats_new/upcoming_changes/array-api/29978.feature.rst
deleted file mode 100644
index 16cbd174a3dfa..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/29978.feature.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :func:`sklearn.metrics.explained_variance_score` and
-  :func:`sklearn.metrics.mean_pinball_loss` now support Array API compatible inputs.
-  By :user:`Virgil Chan `
diff --git a/doc/whats_new/upcoming_changes/array-api/30340.other.rst b/doc/whats_new/upcoming_changes/array-api/30340.other.rst
deleted file mode 100644
index 38053567080f4..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/30340.other.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- array-api-compat and array-api-extra are now vendored within the
-  scikit-learn source. Users of the experimental array API standard
-  support no longer need to install array-api-compat in their environment.
-  by :user:`Lucas Colley `
diff --git a/doc/whats_new/upcoming_changes/array-api/30395.feature.rst b/doc/whats_new/upcoming_changes/array-api/30395.feature.rst
deleted file mode 100644
index 739ea20071dfc..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/30395.feature.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :func:`sklearn.metrics.fbeta_score`,
-  :func:`sklearn.metrics.precision_score` and
-  :func:`sklearn.metrics.recall_score` now support Array API compatible inputs.
-  By :user:`Omar Salman `
diff --git a/doc/whats_new/upcoming_changes/array-api/30819.feature.rst b/doc/whats_new/upcoming_changes/array-api/30819.feature.rst
deleted file mode 100644
index 56955d73ae903..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/30819.feature.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-- :func:`sklearn.utils.extmath.randomized_svd` now support Array API compatible inputs.
-  By :user:`Connor Lane ` and :user:`Jérémie du Boisberranger `.
diff --git a/doc/whats_new/upcoming_changes/array-api/30838.feature.rst b/doc/whats_new/upcoming_changes/array-api/30838.feature.rst
deleted file mode 100644
index f733f1c6476a6..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/30838.feature.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-- :func:`sklearn.metrics.hamming_loss` now support Array API compatible inputs.
-  By :user:`Thomas Li `
diff --git a/doc/whats_new/upcoming_changes/array-api/31190.feature.rst b/doc/whats_new/upcoming_changes/array-api/31190.feature.rst
deleted file mode 100644
index 15504c0e28fce..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/31190.feature.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-- :class:`preprocessing.Binarizer` now supports Array API compatible inputs.
-  By :user:`Yaroslav Korobko `, :user:`Olivier Grisel `, and :user:`Thomas Li `.
diff --git a/doc/whats_new/upcoming_changes/array-api/31204.feature.rst b/doc/whats_new/upcoming_changes/array-api/31204.feature.rst
deleted file mode 100644
index e1e2bc61738ca..0000000000000
--- a/doc/whats_new/upcoming_changes/array-api/31204.feature.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-- :func:`sklearn.metrics.jaccard_score` now supports Array API compatible inputs.
-  By :user:`Omar Salman `
diff --git a/doc/whats_new/upcoming_changes/changed-models/31316.fix.rst b/doc/whats_new/upcoming_changes/changed-models/31316.fix.rst
deleted file mode 100644
index 06071e40affbc..0000000000000
--- a/doc/whats_new/upcoming_changes/changed-models/31316.fix.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- Change the `ConvergenceWarning` message of estimators that rely on the
-  `"lbfgs"` optimizer internally to be more informative and to avoid
-  suggesting to increase the maximum number of iterations when it is not
-  user-settable or when the convergence problem happens before reaching it.
-  By :user:`Olivier Grisel `.
diff --git a/doc/whats_new/upcoming_changes/many-modules/30858.other.rst b/doc/whats_new/upcoming_changes/many-modules/30858.other.rst
deleted file mode 100644
index 5e2441cf5c95e..0000000000000
--- a/doc/whats_new/upcoming_changes/many-modules/30858.other.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-
-- Sparse update: As part of the SciPy change from spmatrix to sparray, all
-  internal use of sparse now supports both sparray and spmatrix.
-  All manipulations of sparse objects should work for either spmatrix or sparray.
-  This is pass 1 of a migration toward sparray (see
-  `SciPy migration to sparray `_
-  By :user:`Dan Schult `
diff --git a/doc/whats_new/upcoming_changes/metadata-routing/30833.feature.rst b/doc/whats_new/upcoming_changes/metadata-routing/30833.feature.rst
deleted file mode 100644
index e46420e9ee2d2..0000000000000
--- a/doc/whats_new/upcoming_changes/metadata-routing/30833.feature.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :class:`ensemble.BaggingClassifier` and :class:`ensemble.BaggingRegressor` now support
-  metadata routing through their `predict`, `predict_proba`, `predict_log_proba` and
-  `decision_function` methods and pass `**params` to the underlying estimators.
-  By :user:`Stefanie Senger `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.base/30763.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.base/30763.enhancement.rst
deleted file mode 100644
index 6a105da88ed0e..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.base/30763.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :class:`base.BaseEstimator` now has a parameter table added to the
-  estimators HTML representation that can be visualized with jupyter.
-  By :user:`Guillaume Lemaitre ` and
-  :user:`Dea María Léon `
diff --git a/doc/whats_new/upcoming_changes/sklearn.calibration/30873.fix.rst b/doc/whats_new/upcoming_changes/sklearn.calibration/30873.fix.rst
deleted file mode 100644
index 3e438622f4918..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.calibration/30873.fix.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-- :class:`~calibration.CalibratedClassifierCV` now raises `FutureWarning`
-  instead of `UserWarning` when passing `cv="prefit`". By
-  :user:`Olivier Grisel `
-- :class:`~calibration.CalibratedClassifierCV` with `method="sigmoid"` no
-  longer crashes when passing `float64`-dtyped `sample_weight` along with a
-  base estimator that outputs `float32`-dtyped predictions. By :user:`Olivier
-  Grisel `
diff --git a/doc/whats_new/upcoming_changes/sklearn.compose/31167.api.rst b/doc/whats_new/upcoming_changes/sklearn.compose/31167.api.rst
deleted file mode 100644
index 5f25cbac65020..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.compose/31167.api.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- The `force_int_remainder_cols` parameter of :class:`compose.ColumnTransformer` and
-  :func:`compose.make_column_transformer` is deprecated and will be removed in 1.9.
-  It has no effect.
-  By :user:`Jérémie du Boisberranger `
diff --git a/doc/whats_new/upcoming_changes/sklearn.covariance/30483.fix.rst b/doc/whats_new/upcoming_changes/sklearn.covariance/30483.fix.rst
deleted file mode 100644
index 4329c5a2696fd..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.covariance/30483.fix.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-- Support for ``n_samples == n_features`` in `sklearn.covariance.MinCovDet` has
-  been restored.  By :user:`Antony Lee `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.datasets/30196.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.datasets/30196.enhancement.rst
deleted file mode 100644
index d044d039badd2..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.datasets/30196.enhancement.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- New parameter ``return_X_y`` added to :func:`datasets.make_classification`. The
-  default value of the parameter does not change how the function behaves.
-  By :user:`Success Moses ` and :user:`Adam Cooper `
diff --git a/doc/whats_new/upcoming_changes/sklearn.decomposition/30443.feature.rst b/doc/whats_new/upcoming_changes/sklearn.decomposition/30443.feature.rst
deleted file mode 100644
index 5678039b69065..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.decomposition/30443.feature.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :class:`~sklearn.decomposition.DictionaryLearning`,
-  :class:`~sklearn.decomposition.SparseCoder`  and
-  :class:`~sklearn.decomposition.MiniBatchDictionaryLearning` now have a
-  ``inverse_transform`` method. By :user:`Rémi Flamary `
diff --git a/doc/whats_new/upcoming_changes/sklearn.ensemble/27124.feature.rst b/doc/whats_new/upcoming_changes/sklearn.ensemble/27124.feature.rst
deleted file mode 100644
index 2087efb00d779..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.ensemble/27124.feature.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-- :class:`ensemble.HistGradientBoostingClassifier` and
-  :class:`ensemble.HistGradientBoostingRegressor` allow for more control over the
-  validation set used for early stopping. You can now pass data to be used for
-  validation directly to `fit` via the arguments `X_val`, `y_val` and
-  `sample_weight_val`.
-  By :user:`Christian Lorentzen `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.ensemble/30649.fix.rst b/doc/whats_new/upcoming_changes/sklearn.ensemble/30649.fix.rst
deleted file mode 100644
index 43ad381fb5ca8..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.ensemble/30649.fix.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-- :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor`
-  validate `estimators` to make sure it is a list of tuples. By `Thomas Fan`_.
diff --git a/doc/whats_new/upcoming_changes/sklearn.feature_selection/30179.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.feature_selection/30179.enhancement.rst
deleted file mode 100644
index 6eec68c0d95e7..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.feature_selection/30179.enhancement.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :class:`feature_selection.RFECV` now gives access to the ranking and support in each
-  iteration and cv step of feature selection.
-  By :user:`Marie S. `
diff --git a/doc/whats_new/upcoming_changes/sklearn.feature_selection/31107.fix.rst b/doc/whats_new/upcoming_changes/sklearn.feature_selection/31107.fix.rst
deleted file mode 100644
index b5ca4ab283434..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.feature_selection/31107.fix.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :class:`feature_selection.SelectFromModel` now correctly works when the estimator
-  is an instance of :class:`linear_model.ElasticNetCV` with its `l1_ratio` parameter
-  being an array-like.
-  By :user:`Vasco Pereira `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.gaussian_process/22227.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.gaussian_process/22227.enhancement.rst
deleted file mode 100644
index bcc9825f30978..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.gaussian_process/22227.enhancement.rst
+++ /dev/null
@@ -1 +0,0 @@
-- :class:`gaussian_process.GaussianProcessClassifier` now includes a `latent_mean_and_variance` method that exposes the mean and the variance of the latent function, :math:`f`, used in the Laplace approximation. By :user:`Miguel González Duque `
diff --git a/doc/whats_new/upcoming_changes/sklearn.inspection/26202.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.inspection/26202.enhancement.rst
deleted file mode 100644
index 666d55a24c577..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.inspection/26202.enhancement.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- Add `custom_values` parameter in :func:`inspection.partial_dependence`. It enables
-  users to pass their own grid of values at which the partial dependence should be
-  calculated.
-  By :user:`Freddy A. Boulton ` and :user:`Stephen Pardy
-  `
diff --git a/doc/whats_new/upcoming_changes/sklearn.inspection/29797.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.inspection/29797.enhancement.rst
deleted file mode 100644
index 2b16d7e2bf6be..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.inspection/29797.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :class:`inspection.DecisionBoundaryDisplay` now supports
-  plotting all classes for multi-class problems when `response_method` is
-  'decision_function', 'predict_proba' or 'auto'.
-  By :user:`Lucy Liu `
diff --git a/doc/whats_new/upcoming_changes/sklearn.inspection/30409.api.rst b/doc/whats_new/upcoming_changes/sklearn.inspection/30409.api.rst
deleted file mode 100644
index ab73ed4bd1afd..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.inspection/30409.api.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- :func:`inspection.partial_dependence` does no longer accept integer dtype for
-  numerical feature columns. Explicit conversion to floating point values is
-  now required before calling this tool (and preferably even before fitting the
-  model to inspect).
-  By :user:`Olivier Grisel `
diff --git a/doc/whats_new/upcoming_changes/sklearn.inspection/31146.fix.rst b/doc/whats_new/upcoming_changes/sklearn.inspection/31146.fix.rst
deleted file mode 100644
index 2cd7d6eed61f5..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.inspection/31146.fix.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :func:`inspection.partial_dependence` now raises an informative error when passing
-  an empty list as the `categorical_features` parameter. `None` should be used instead
-  to indicate that no categorical features are present.
-  By :user:`Pedro Lopes `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/30057.fix.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/30057.fix.rst
deleted file mode 100644
index 94ed332295b9b..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/30057.fix.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- :class:`linear_model.LogisticRegression` and
-  :class:`linear_model.LogisticRegressionCV` now properly pass sample weights to
-  :func:`utils.class_weight.compute_class_weight` when fit with
-  `class_weight="balanced"`.
-  By :user:`Shruti Nath ` and :user:`Olivier Grisel `
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/30521.fix.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/30521.fix.rst
deleted file mode 100644
index 951da8f2627b4..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/30521.fix.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- Added a new parameter `tol` to
-  :class:`linear_model.LinearRegression` that determines the precision of the
-  solution `coef_` when fitting on sparse data.
-  By :user:`Success Moses `
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/30616.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/30616.api.rst
deleted file mode 100644
index 2b9d30e445bcf..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/30616.api.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-- The parameter `n_alphas` has been deprecated in the following classes:
-  :class:`linear_model.ElasticNetCV` and :class:`linear_model.LassoCV`
-  and :class:`linear_model.MultiTaskElasticNetCV`
-  and :class:`linear_model.MultiTaskLassoCV`, and will be removed in 1.9. The parameter
-  `alphas` now supports both integers and array-likes, removing the need for `n_alphas`.
-  From now on, only `alphas` should be set to either indicate the number of alphas to
-  automatically generate (int) or to provide a list of alphas (array-like) to test along
-  the regularization path.
-  By :user:`Siddharth Bansal `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/30644.fix.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/30644.fix.rst
deleted file mode 100644
index 9c8a85b080617..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/30644.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- The update and initialization of the hyperparameters now properly handle
-  sample weights in :class:`linear_model.BayesianRidge`.
-  By :user:`Antoine Baker `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/30730.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/30730.enhancement.rst
deleted file mode 100644
index 91638cbcd9c7a..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/30730.enhancement.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :class:`linear_model.SGDClassifier` and :class:`linear_model.SGDRegressor` now accept
-  `l1_ratio=None` when `penalty` is not `"elasticnet"`.
-  By :user:`Marc Bresson `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31094.fix.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31094.fix.rst
deleted file mode 100644
index b65d96bccd7d2..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31094.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :class:`linear_model.BayesianRidge` now uses the full SVD to correctly estimate
-  the posterior covariance matrix `sigma_` when `n_samples < n_features`.
-  By :user:`Antoine Baker `
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31241.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31241.api.rst
deleted file mode 100644
index 9cd97143e29c7..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31241.api.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-- Using the `"liblinear"` solver for multiclass classification with a one-versus-rest
-  scheme in :class:`linear_model.LogisticRegression` and
-  :class:`linear_model.LogisticRegressionCV` is deprecated and will raise an error in
-  version 1.8. Either use a solver which supports the multinomial loss or wrap the
-  estimator in a :class:`sklearn.multiclass.OneVsRestClassifier` to keep applying a
-  one-versus-rest scheme.
-  By :user:`Jérémie du Boisberranger `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst
deleted file mode 100644
index 8b8751347b843..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- Fitting :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet` with
-  `fit_intercept=True` is faster for sparse input `X` because an unnecessary
-  re-computation of the sum of residuals is avoided.
-  By :user:`Christian Lorentzen `
diff --git a/doc/whats_new/upcoming_changes/sklearn.manifold/30514.fix.rst b/doc/whats_new/upcoming_changes/sklearn.manifold/30514.fix.rst
deleted file mode 100644
index 7f4e4104446dc..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.manifold/30514.fix.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :class:`manifold.MDS` now correctly handles non-metric MDS. Furthermore,
-  the returned stress value now corresponds to the returned embedding and
-  normalized stress is now allowed for metric MDS.
-  By :user:`Dmitry Kobak `
diff --git a/doc/whats_new/upcoming_changes/sklearn.manifold/31117.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.manifold/31117.enhancement.rst
deleted file mode 100644
index 87b6896890163..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.manifold/31117.enhancement.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :class:`manifold.MDS` will switch to use `n_init=1` by default,
-  starting from version 1.9.
-  By :user:`Dmitry Kobak `
diff --git a/doc/whats_new/upcoming_changes/sklearn.manifold/31117.fix.rst b/doc/whats_new/upcoming_changes/sklearn.manifold/31117.fix.rst
deleted file mode 100644
index 6248a23b86546..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.manifold/31117.fix.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- :class:`manifold.MDS` now uses `eps=1e-6` by default and the convergence
-  criterion was adjusted to make sense for both metric and non-metric MDS
-  and to follow the reference R implementation. The formula for normalized
-  stress was adjusted to follow the original definition by Kruskal.
-  By :user:`Dmitry Kobak `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/22046.feature.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/22046.feature.rst
deleted file mode 100644
index dbe9166aa1314..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/22046.feature.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-- :func:`metrics.brier_score_loss` implements the Brier score for multiclass
-  classification problems and adds a `scale_by_half` argument. This metric is
-  notably useful to assess both sharpness and calibration of probabilistic
-  classifiers. See the docstrings for more details. By
-  :user:`Varun Aggarwal `, :user:`Olivier Grisel ` and
-  :user:`Antoine Baker `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/22046.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/22046.fix.rst
deleted file mode 100644
index 7ba041f2686cf..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/22046.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :func:`metrics.log_loss` now raises a `ValueError` if values of `y_true`
-  are missing in `labels`. By :user:`Varun Aggarwal `,
-  :user:`Olivier Grisel ` and :user:`Antoine Baker `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/28981.api.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/28981.api.rst
deleted file mode 100644
index 6cc771d6a0d45..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/28981.api.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- The `sparse` parameter of :func:`metrics.fowlkes_mallows_score` is deprecated and
-  will be removed in 1.9. It has no effect.
-  By :user:`Luc Rocher `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/29151.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/29151.enhancement.rst
deleted file mode 100644
index fc552703f2512..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/29151.enhancement.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-- :func:`metrics.det_curve`, :class:`metrics.DetCurveDisplay.from_estimator`,
-  and :class:`metrics.DetCurveDisplay.from_estimator` now accept a
-  `drop_intermediate` option to drop thresholds where true positives (tp) do not
-  change from the previous or subsequent thresholds. All points with the same tp
-  value have the same `fnr` and thus same y coordinate in a DET curve.
-  By :user:`Arturo Amor `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/29151.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/29151.fix.rst
deleted file mode 100644
index 61cf97e9b27f6..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/29151.fix.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :func:`metrics.det_curve` and :class:`metrics.DetCurveDisplay` now return an
-  extra threshold at infinity where the classifier always predicts the negative
-  class i.e. tps = fps = 0.
-  By :user:`Arturo Amor `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/29288.api.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/29288.api.rst
deleted file mode 100644
index 1c8e15d714391..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/29288.api.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- The `raise_warning` parameter of :func:`metrics.class_likelihood_ratios` is deprecated
-  and will be removed in 1.9. An `UndefinedMetricWarning` will always be raised in case
-  of a division by zero.
-  By :user:`Stefanie Senger `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/29288.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/29288.enhancement.rst
deleted file mode 100644
index e6e682a333f86..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/29288.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :func:`~metrics.class_likelihood_ratios` now has a `replace_undefined_by` param.
-  When there is a division by zero, the metric is undefined and the set values are
-  returned for `LR+` and `LR-`.
-  By :user:`Stefanie Senger `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/29288.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/29288.fix.rst
deleted file mode 100644
index 23237b3923668..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/29288.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :func:`~metrics.class_likelihood_ratios` now raises `UndefinedMetricWarning` instead
-  of `UserWarning` when a division by zero occurs.
-  By :user:`Stefanie Senger `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/29727.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/29727.fix.rst
deleted file mode 100644
index b25de83128504..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/29727.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :class:`metrics.RocCurveDisplay` will no longer set a legend when
-  `label` is `None` in both the `line_kwargs` and the `chance_level_kw`.
-  By :user:`Arturo Amor `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/29865.api.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/29865.api.rst
deleted file mode 100644
index 60ea7d83de71f..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/29865.api.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- In :meth:`sklearn.metrics.RocCurveDisplay.from_predictions`,
-  the argument `y_pred` has been renamed to `y_score` to better reflect its purpose.
-  `y_pred` will be removed in 1.9.
-  By :user:`Bagus Tris Atmaja ` in
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst
deleted file mode 100644
index c3b6d77c5aefb..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/30399.feature.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- Add class method `from_cv_results` to :class:`metrics.RocCurveDisplay`, which allows
-  easy plotting of multiple ROC curves from :func:`model_selection.cross_validate`
-  results.
-  By :user:`Lucy Liu `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst
deleted file mode 100644
index ec0418b290040..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-- Additional `sample_weight` checking has been added to
-  :func:`metrics.mean_absolute_error`,
-  :func:`metrics.mean_pinball_loss`,
-  :func:`metrics.mean_absolute_percentage_error`,
-  :func:`metrics.mean_squared_error`,
-  :func:`metrics.root_mean_squared_error`,
-  :func:`metrics.mean_squared_log_error`,
-  :func:`metrics.root_mean_squared_log_error`,
-  :func:`metrics.explained_variance_score`,
-  :func:`metrics.r2_score`,
-  :func:`metrics.mean_tweedie_deviance`,
-  :func:`metrics.mean_poisson_deviance`,
-  :func:`metrics.mean_gamma_deviance` and
-  :func:`metrics.d2_tweedie_score`.
-  `sample_weight` can only be 1D, consistent to `y_true` and `y_pred` in length
-  or a scalar.
-  By :user:`Lucy Liu `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/30903.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/30903.fix.rst
deleted file mode 100644
index 90250f427dc20..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/30903.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :func:`~metrics.d2_log_loss_score` now properly handles the case when `labels` is
-  passed and not all of the labels are present in `y_true`.
-  By :user:`Vassilis Margonis `
diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/31065.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/31065.fix.rst
deleted file mode 100644
index 82126da7852cc..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.metrics/31065.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- Fix :func:`metrics.adjusted_mutual_info_score` numerical issue when number of
-  classes and samples is low.
-  By :user:`Hleb Levitski `
diff --git a/doc/whats_new/upcoming_changes/sklearn.mixture/28559.feature.rst b/doc/whats_new/upcoming_changes/sklearn.mixture/28559.feature.rst
deleted file mode 100644
index 31da86d63c0f7..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.mixture/28559.feature.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- Added an attribute `lower_bounds_` in the :class:`mixture.BaseMixture`
-  class to save the list of lower bounds for each iteration thereby providing
-  insights into the convergence behavior of mixture models like
-  :class:`mixture.GaussianMixture`.
-  By :user:`Manideep Yenugula `
diff --git a/doc/whats_new/upcoming_changes/sklearn.mixture/30414.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.mixture/30414.efficiency.rst
deleted file mode 100644
index 401ebb65916bb..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.mixture/30414.efficiency.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- Simplified redundant computation when estimating covariances in
-  :class:`~mixture.GaussianMixture` with a `covariance_type="spherical"` or
-  `covariance_type="diag"`.
-  By :user:`Leonce Mekinda ` and :user:`Olivier Grisel `
diff --git a/doc/whats_new/upcoming_changes/sklearn.mixture/30415.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.mixture/30415.efficiency.rst
deleted file mode 100644
index 095ef66ce28c0..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.mixture/30415.efficiency.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- :class:`~mixture.GaussianMixture` now consistently operates at `float32`
-  precision when fitted with `float32` data to improve training speed and
-  memory efficiency. Previously, part of the computation would be implicitly
-  cast to `float64`. By :user:`Olivier Grisel ` and :user:`Omar Salman
-  `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.model_selection/30743.fix.rst b/doc/whats_new/upcoming_changes/sklearn.model_selection/30743.fix.rst
deleted file mode 100644
index 8e091f55b2e31..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.model_selection/30743.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- Hyper-parameter optimizers such as :class:`model_selection.GridSearchCV`
-  now forward `sample_weight` to the scorer even when metadata routing is not enabled.
-  By :user:`Antoine Baker `
diff --git a/doc/whats_new/upcoming_changes/sklearn.multiclass/31228.fix.rst b/doc/whats_new/upcoming_changes/sklearn.multiclass/31228.fix.rst
deleted file mode 100644
index 68056db580fd7..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.multiclass/31228.fix.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- The `predict_proba` method of :class:`sklearn.multiclass.OneVsRestClassifier` now
-  returns zero for all classes when all inner estimators never predict their positive
-  class.
-  By :user:`Luis M. B. Varona `, :user:`Marc Bresson `, and
-  :user:`Jérémie du Boisberranger `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.multioutput/30152.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.multioutput/30152.enhancement.rst
deleted file mode 100644
index 3bc2ae2f6ced4..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.multioutput/30152.enhancement.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- The parameter `base_estimator` has been deprecated in favour of `estimator` for
-  :class:`multioutput.RegressorChain` and :class:`multioutput.ClassifierChain`.
-  By :user:`Success Moses ` and :user:`dikraMasrour `
diff --git a/doc/whats_new/upcoming_changes/sklearn.neural_network/24788.fix.rst b/doc/whats_new/upcoming_changes/sklearn.neural_network/24788.fix.rst
deleted file mode 100644
index dc2742e9a04d8..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.neural_network/24788.fix.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :class:`neural_network.MLPRegressor` now raises an informative error when
-  `early_stopping` is set and the computed validation set is too small.
-  By :user:`David Shumway `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.neural_network/30155.feature.rst b/doc/whats_new/upcoming_changes/sklearn.neural_network/30155.feature.rst
deleted file mode 100644
index 4fcf738072e5e..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.neural_network/30155.feature.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- Added support for `sample_weight` in :class:`neural_network.MLPClassifier` and
-  :class:`neural_network.MLPRegressor`.
-  By :user:`Zach Shu ` and :user:`Christian Lorentzen `
diff --git a/doc/whats_new/upcoming_changes/sklearn.neural_network/30712.feature.rst b/doc/whats_new/upcoming_changes/sklearn.neural_network/30712.feature.rst
deleted file mode 100644
index e8ad9882ff0f0..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.neural_network/30712.feature.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- Added parameter for `loss` in :class:`neural_network.MLPRegressor` with options
-  `"squared_error"` (default) and `"poisson"` (new).
-  By :user:`Christian Lorentzen `
diff --git a/doc/whats_new/upcoming_changes/sklearn.pipeline/30406.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.pipeline/30406.enhancement.rst
deleted file mode 100644
index 8e2a5f6242392..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.pipeline/30406.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- Expose the ``verbose_feature_names_out`` argument in the
-  :func:`pipeline.make_union` function, allowing users to control
-  feature name uniqueness in the :class:`pipeline.FeatureUnion`.
-  By :user:`Abhijeetsingh Meena `
diff --git a/doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.enhancement.rst
deleted file mode 100644
index 0ce9249cc94fb..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.enhancement.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-- :class:`preprocessing.KBinsDiscretizer` with `strategy="uniform"` now
-  accepts `sample_weight`. Additionally with `strategy="quantile"` the
-  `quantile_method` can now be specified (in the future
-  `quantile_method="averaged_inverted_cdf"` will become the default).
-  By :user:`Shruti Nath ` and :user:`Olivier Grisel
-  `
diff --git a/doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.fix.rst b/doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.fix.rst
deleted file mode 100644
index d2f61e099c5eb..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.preprocessing/29907.fix.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-- :class:`preprocessing.KBinsDiscretizer` now uses weighted resampling when
-  sample weights are given and subsampling is used. This may change results
-  even when not using sample weights, although in absolute and not in terms
-  of statistical properties.
-  By :user:`Shruti Nath ` and :user:`Jérémie du Boisberranger
-  `
diff --git a/doc/whats_new/upcoming_changes/sklearn.preprocessing/31227.fix.rst b/doc/whats_new/upcoming_changes/sklearn.preprocessing/31227.fix.rst
deleted file mode 100644
index 803517760a822..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.preprocessing/31227.fix.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-- Now using ``scipy.stats.yeojohnson`` instead of our own implementation of the Yeo-Johnson transform.
-  Fixed numerical stability (mostly overflows) of the Yeo-Johnson transform with
-  `PowerTransformer(method="yeo-johnson")` when scipy version is `>= 1.12`.
-  Initial PR by :user:`Xuefeng Xu ` completed by :user:`Mohamed Yaich `,
-  :user:`Oussama Er-rabie `, :user:`Mohammed Yaslam Dlimi `,
-  :user:`Hamza Zaroual `, :user:`Amine Hannoun ` and :user:`Sylvain Marié `.
\ No newline at end of file
diff --git a/doc/whats_new/upcoming_changes/sklearn.svm/30057.fix.rst b/doc/whats_new/upcoming_changes/sklearn.svm/30057.fix.rst
deleted file mode 100644
index 5951e0dd2a0c0..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.svm/30057.fix.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :class:`svm.LinearSVC` now properly passes sample weights to
-  :func:`utils.class_weight.compute_class_weight` when fit with
-  `class_weight="balanced"`.
-  By :user:`Shruti Nath `
diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/26335.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/26335.enhancement.rst
deleted file mode 100644
index 9a82ab4f02675..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.utils/26335.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :func:`utils.multiclass.type_of_target` raises a warning when the number
-  of unique classes is greater than 50% of the number of samples. This warning is raised
-  only if `y` has more than 20 samples.
-  By :user:`Rahil Parikh `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/29907.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/29907.enhancement.rst
deleted file mode 100644
index 0a17e5d1d1ae1..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.utils/29907.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :func: `resample` now handles sample weights which allows
-  weighted resampling.
-  By :user:`Shruti Nath ` and :user:`Olivier Grisel
-  `
diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/30057.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/30057.enhancement.rst
deleted file mode 100644
index 8ca10c884c9b3..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.utils/30057.enhancement.rst
+++ /dev/null
@@ -1,3 +0,0 @@
-- :func:`utils.class_weight.compute_class_weight` now properly accounts for
-  sample weights when using strategy "balanced" to calculate class weights.
-  By :user:`Shruti Nath `
diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/30380.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/30380.enhancement.rst
deleted file mode 100644
index bd1eaf9213257..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.utils/30380.enhancement.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-- Warning filters from the main process are propagated to joblib workers.
-  By `Thomas Fan`_
diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/30775.fix.rst b/doc/whats_new/upcoming_changes/sklearn.utils/30775.fix.rst
deleted file mode 100644
index bd383a70c2bba..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.utils/30775.fix.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-- In :mod:`utils.estimator_checks` we now enforce for binary classifiers a
-  binary `y` by taking the minimum as the negative class instead of the first
-  element, which makes it robust to `y` shuffling. It prevents two checks from
-  wrongly failing on binary classifiers.
-  By :user:`Antoine Baker `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/30819.fix.rst b/doc/whats_new/upcoming_changes/sklearn.utils/30819.fix.rst
deleted file mode 100644
index 81c7564023ac1..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.utils/30819.fix.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- :func:`utils.extmath.randomized_svd` and :func:`utils.extmath.randomized_range_finder`
-  now validate their input array to fail early with an informative error message on
-  invalid input.
-  By :user:`Connor Lane `.
diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31040.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31040.enhancement.rst
deleted file mode 100644
index 096a98cb176bc..0000000000000
--- a/doc/whats_new/upcoming_changes/sklearn.utils/31040.enhancement.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-- The private helper function :func:`utils._safe_indexing` now officially supports
-  pyarrow data. For instance, passing a pyarrow `Table` as `X` in a
-  :class:`compose.ColumnTransformer` is now possible.
-  By :user:`Christian Lorentzen `
diff --git a/doc/whats_new/v1.7.rst b/doc/whats_new/v1.7.rst
index 35a528f3e8311..ab022414982ff 100644
--- a/doc/whats_new/v1.7.rst
+++ b/doc/whats_new/v1.7.rst
@@ -8,19 +8,8 @@
 Version 1.7
 ===========
 
-..
-  -- UNCOMMENT WHEN 1.7.0 IS RELEASED --
-  For a short description of the main highlights of the release, please refer to
-  :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_6_0.py`.
-
-
-..
-  DELETE WHEN 1.7.0 IS RELEASED
-  Since October 2024, DO NOT add your changelog entry in this file.
-..
-  Instead, create a file named `..rst` in the relevant sub-folder in
-  `doc/whats_new/upcoming_changes/`. For full details, see:
-  https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/upcoming_changes/README.md
+For a short description of the main highlights of the release, please refer to
+:ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_7_0.py`.
 
 .. include:: changelog_legend.inc
 
@@ -31,12 +20,20 @@ Version 1.7
 Version 1.7.0
 =============
 
-**May 2025**
+**June 2025**
+
+Changed models
+--------------
+
+- |Fix| Change the `ConvergenceWarning` message of estimators that rely on the
+  `"lbfgs"` optimizer internally to be more informative and to avoid
+  suggesting to increase the maximum number of iterations when it is not
+  user-settable or when the convergence problem happens before reaching it.
+  By :user:`Olivier Grisel `. :pr:`31316`
 
 Changes impacting many modules
 ------------------------------
 
-
 - Sparse update: As part of the SciPy change from spmatrix to sparray, all
   internal use of sparse now supports both sparray and spmatrix.
   All manipulations of sparse objects should work for either spmatrix or sparray.
@@ -74,6 +71,9 @@ See :ref:`array_api` for more details.
 - |Feature| :class:`preprocessing.Binarizer` now supports Array API compatible inputs.
   By :user:`Yaroslav Korobko `, :user:`Olivier Grisel `, and :user:`Thomas Li `. :pr:`31190`
 
+- |Feature| :func:`sklearn.metrics.jaccard_score` now supports Array API compatible inputs.
+  By :user:`Omar Salman ` :pr:`31204`
+
 - array-api-compat and array-api-extra are now vendored within the
   scikit-learn source. Users of the experimental array API standard
   support no longer need to install array-api-compat in their environment.
@@ -90,6 +90,14 @@ more details.
   `decision_function` methods and pass `**params` to the underlying estimators.
   By :user:`Stefanie Senger `. :pr:`30833`
 
+:mod:`sklearn.base`
+-------------------
+
+- |Enhancement| :class:`base.BaseEstimator` now has a parameter table added to the
+  estimators HTML representation that can be visualized with jupyter.
+  By :user:`Guillaume Lemaitre ` and
+  :user:`Dea María Léon ` :pr:`30763`
+
 :mod:`sklearn.calibration`
 --------------------------
 
@@ -192,6 +200,11 @@ more details.
   `l1_ratio=None` when `penalty` is not `"elasticnet"`.
   By :user:`Marc Bresson `. :pr:`30730`
 
+- |Enhancement| Fitting :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet` with
+  `fit_intercept=True` is faster for sparse input `X` because an unnecessary
+  re-computation of the sum of residuals is avoided.
+  By :user:`Christian Lorentzen ` :pr:`31387`
+
 - |Fix| :class:`linear_model.LogisticRegression` and
   :class:`linear_model.LogisticRegressionCV` now properly pass sample weights to
   :func:`utils.class_weight.compute_class_weight` when fit with
@@ -257,6 +270,11 @@ more details.
   :user:`Varun Aggarwal `, :user:`Olivier Grisel ` and
   :user:`Antoine Baker `. :pr:`22046`
 
+- |Feature| Add class method `from_cv_results` to :class:`metrics.RocCurveDisplay`, which allows
+  easy plotting of multiple ROC curves from :func:`model_selection.cross_validate`
+  results.
+  By :user:`Lucy Liu ` :pr:`30399`
+
 - |Enhancement| :func:`metrics.det_curve`, :class:`metrics.DetCurveDisplay.from_estimator`,
   and :class:`metrics.DetCurveDisplay.from_estimator` now accept a
   `drop_intermediate` option to drop thresholds where true positives (tp) do not
@@ -286,6 +304,24 @@ more details.
   `label` is `None` in both the `line_kwargs` and the `chance_level_kw`.
   By :user:`Arturo Amor ` :pr:`29727`
 
+- |Fix| Additional `sample_weight` checking has been added to
+  :func:`metrics.mean_absolute_error`,
+  :func:`metrics.mean_pinball_loss`,
+  :func:`metrics.mean_absolute_percentage_error`,
+  :func:`metrics.mean_squared_error`,
+  :func:`metrics.root_mean_squared_error`,
+  :func:`metrics.mean_squared_log_error`,
+  :func:`metrics.root_mean_squared_log_error`,
+  :func:`metrics.explained_variance_score`,
+  :func:`metrics.r2_score`,
+  :func:`metrics.mean_tweedie_deviance`,
+  :func:`metrics.mean_poisson_deviance`,
+  :func:`metrics.mean_gamma_deviance` and
+  :func:`metrics.d2_tweedie_score`.
+  `sample_weight` can only be 1D, consistent to `y_true` and `y_pred` in length
+  or a scalar.
+  By :user:`Lucy Liu `. :pr:`30886`
+
 - |Fix| :func:`~metrics.d2_log_loss_score` now properly handles the case when `labels` is
   passed and not all of the labels are present in `y_true`.
   By :user:`Vassilis Margonis ` :pr:`30903`
@@ -445,6 +481,34 @@ more details.
 .. rubric:: Code and documentation contributors
 
 Thanks to everyone who has contributed to the maintenance and improvement of
-the project since version 1.7, including:
-
-TODO: update at the time of the release.
+the project since version 1.6, including:
+
+4hm3d, Aaron Schumacher, Abhijeetsingh Meena, Acciaro Gennaro Daniele, 
+Achraf Tasfaout, Adrien Linares, Adrin Jalali, Agriya Khetarpal, Aiden Frank, 
+Aitsaid Azzedine Idir, ajay-sentry, Akanksha Mhadolkar, Alfredo Saucedo, 
+Anderson Chaves, Andres Guzman-Ballen, Aniruddha Saha, antoinebaker, Antony 
+Lee, Arjun S, ArthurDbrn, Arturo, Arturo Amor, ash, Ashton Powell, 
+ayoub.agouzoul, Bagus Tris Atmaja, Benjamin Danek, Boney Patel, Camille 
+Troillard, Chems Ben, Christian Lorentzen, Christian Veenhuis, Christine P. 
+Chai, claudio, Code_Blooded, Colas, Colin Coe, Connor Lane, Corey Farwell, 
+Daniel Agyapong, Dan Schult, Dea María Léon, Deepak Saldanha, 
+dependabot[bot], Dimitri Papadopoulos Orfanos, Dmitry Kobak, Domenico, Elham 
+Babaei, emelia-hdz, EmilyXinyi, Emma Carballal, Eric Larson, fabianhenning, 
+Gael Varoquaux, Gil Ramot, Gordon Grey, Goutam, G Sreeja, Guillaume Lemaitre, 
+Haesun Park, Hanjun Kim, Helder Geovane Gomes de Lima, Henri Bonamy, Hleb 
+Levitski, Hugo Boulenger, IlyaSolomatin, Irene, Jérémie du Boisberranger, 
+Jérôme Dockès, JoaoRodriguesIST, Joel Nothman, Josh, Kevin Klein, Loic 
+Esteve, Lucas Colley, Luc Rocher, Lucy Liu, Luis M. B. Varona, lunovian, Mamduh 
+Zabidi, Marc Bresson, Marco Edward Gorelli, Marco Maggi, Maren Westermann, 
+Marie Sacksick, Martin Jurča, Miguel González Duque, Mihir Waknis, Mohamed 
+Ali SRIR, Mohamed DHIFALLAH, mohammed benyamna, Mohit Singh Thakur, Mounir 
+Lbath, myenugula, Natalia Mokeeva, Olivier Grisel, omahs, Omar Salman, Pedro 
+Lopes, Pedro Olivares, Preyas Shah, Radovenchyk, Rahil Parikh, Rémi Flamary, 
+Reshama Shaikh, Rishab Saini, rolandrmgservices, SanchitD, Santiago Castro, 
+Santiago Víquez, scikit-learn-bot, Scott Huberty, Shruti Nath, Siddharth 
+Bansal, Simarjot Sidhu, Sortofamudkip, sotagg, Sourabh Kumar, Stefan, Stefanie 
+Senger, Stefano Gaspari, Stephen Pardy, Success Moses, Sylvain Combettes, Tahar 
+Allouche, Thomas J. Fan, Thomas Li, ThorbenMaa, Tim Head, Umberto Fasci, UV, 
+Vasco Pereira, Vassilis Margonis, Velislav Babatchev, Victoria Shevchenko, 
+viktor765, Vipsa Kamani, Virgil Chan, vpz, Xiao Yuan, Yaich Mohamed, Yair 
+Shimony, Yao Xiao, Yaroslav Halchenko, Yulia Vilensky, Yuvi Panda

From 1a9aded9b23f5278be708a2229dfbab4f6a17ac9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?=
 
Date: Fri, 30 May 2025 15:33:11 +0200
Subject: [PATCH 60/64] [cd build]


From 6d202cdce79cc7aa9656576d5eb9099bd4655557 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?=
 
Date: Wed, 4 Jun 2025 19:14:27 +0200
Subject: [PATCH 61/64] DOC Release highlights for 1.7 (#31469)

Co-authored-by: Guillaume Lemaitre 
---
 .../plot_release_highlights_1_7_0.py          | 115 ++++++++++++++++++
 1 file changed, 115 insertions(+)
 create mode 100644 examples/release_highlights/plot_release_highlights_1_7_0.py

diff --git a/examples/release_highlights/plot_release_highlights_1_7_0.py b/examples/release_highlights/plot_release_highlights_1_7_0.py
new file mode 100644
index 0000000000000..06c2f10e70b28
--- /dev/null
+++ b/examples/release_highlights/plot_release_highlights_1_7_0.py
@@ -0,0 +1,115 @@
+# ruff: noqa: CPY001
+"""
+=======================================
+Release Highlights for scikit-learn 1.7
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.7! Many bug fixes
+and improvements were added, as well as some key new features. Below we
+detail the highlights of this release. **For an exhaustive list of
+all the changes**, please refer to the :ref:`release notes `.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# Improved estimator's HTML representation
+# ----------------------------------------
+# The HTML representation of estimators now includes a section containing the list of
+# parameters and their values. Non-default parameters are highlighted in orange. A copy
+# button is also available to copy the "fully-qualified" parameter name without the
+# need to call the `get_params` method. It is particularly useful when defining a
+# parameter grid for a grid-search or a randomized-search with a complex pipeline.
+#
+# See the example below and click on the different estimator's blocks to see the
+# improved HTML representation.
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+model = make_pipeline(StandardScaler(with_std=False), LogisticRegression(C=2.0))
+model
+
+# %%
+# Custom validation set for histogram-based Gradient Boosting estimators
+# ----------------------------------------------------------------------
+# The :class:`ensemble.HistGradientBoostingClassifier` and
+# :class:`ensemble.HistGradientBoostingRegressor` now support directly passing a custom
+# validation set for early stopping to the `fit` method, using the `X_val`, `y_val`, and
+# `sample_weight_val` parameters.
+# In a :class:`pipeline.Pipeline`, the validation set `X_val` can be transformed along
+# with `X` using the `transform_input` parameter.
+
+import sklearn
+from sklearn.datasets import make_classification
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+
+sklearn.set_config(enable_metadata_routing=True)
+
+X, y = make_classification(random_state=0)
+X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=0)
+
+clf = HistGradientBoostingClassifier()
+clf.set_fit_request(X_val=True, y_val=True)
+
+model = Pipeline([("sc", StandardScaler()), ("clf", clf)], transform_input=["X_val"])
+model.fit(X, y, X_val=X_val, y_val=y_val)
+
+# %%
+# Plotting ROC curves from cross-validation results
+# -------------------------------------------------
+# The class :class:`metrics.RocCurveDisplay` has a new class method `from_cv_results`
+# that allows to easily plot multiple ROC curves from the results of
+# :func:`model_selection.cross_validate`.
+
+from sklearn.datasets import make_classification
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import RocCurveDisplay
+from sklearn.model_selection import cross_validate
+
+X, y = make_classification(n_samples=150, random_state=0)
+clf = LogisticRegression(random_state=0)
+cv_results = cross_validate(clf, X, y, cv=5, return_estimator=True, return_indices=True)
+_ = RocCurveDisplay.from_cv_results(cv_results, X, y)
+
+# %%
+# Array API support
+# -----------------
+# Several functions have been updated to support array API compatible inputs since
+# version 1.6, especially metrics from the :mod:`sklearn.metrics` module.
+#
+# In addition, it is no longer required to install the `array-api-compat` package to use
+# the experimental array API support in scikit-learn.
+#
+# Please refer to the :ref:`array API support` page for instructions to use
+# scikit-learn with array API compatible libraries such as PyTorch or CuPy.
+
+# %%
+# Improved API consistency of Multi-layer Perceptron
+# --------------------------------------------------
+# The :class:`neural_network.MLPRegressor` has a new parameter `loss` and now supports
+# the "poisson" loss in addition to the default "squared_error" loss.
+# Moreover, the :class:`neural_network.MLPClassifier` and
+# :class:`neural_network.MLPRegressor` estimators now support sample weights.
+# These improvements have been made to improve the consistency of these estimators
+# with regard to the other estimators in scikit-learn.
+
+# %%
+# Migration toward sparse arrays
+# ------------------------------
+# In order to prepare `SciPy migration from sparse matrices to sparse arrays `_,
+# all scikit-learn estimators that accept sparse matrices as input now also accept
+# sparse arrays.

From ac7acc9929cc57343da2c0bf9955013f0318ba35 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?=
 
Date: Wed, 4 Jun 2025 19:17:24 +0200
Subject: [PATCH 62/64] [cd build]


From 8521dc298ad77e80f5093221382773af69d7fa35 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?=
 
Date: Thu, 5 Jun 2025 01:31:38 +0200
Subject: [PATCH 63/64] DOC Don't use deprecated RocCurveDisplay kwargs
 (#31482)

---
 doc/visualizations.rst | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/doc/visualizations.rst b/doc/visualizations.rst
index e42be3a6db040..e9d38f25e1e0d 100644
--- a/doc/visualizations.rst
+++ b/doc/visualizations.rst
@@ -100,8 +100,10 @@ again by using the `plot` method of the `Display` object.
     rfc.fit(X_train, y_train)
 
     ax = plt.gca()
-    rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
-    clf_disp.plot(ax=ax, alpha=0.8)
+    rfc_disp = RocCurveDisplay.from_estimator(
+      rfc, X_test, y_test, ax=ax, curve_kwargs={"alpha": 0.8}
+    )
+    clf_disp.plot(ax=ax, curve_kwargs={"alpha": 0.8})
 
 Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha
 values of the curves.

From be4208bab0addaf3d30b8d09e2775bd99a4e8c52 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?=
 
Date: Thu, 5 Jun 2025 01:32:51 +0200
Subject: [PATCH 64/64] [cd build]