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‎doc/modules/linear_model.rst

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@@ -654,7 +654,7 @@ or :func:`lars_path_gram`.
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.. rubric:: References
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* Original Algorithm is detailed in the paper `Least Angle Regression
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<https://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf>`_
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<https://hastie.su.domains/Papers/LARS/LeastAngle_2002.pdf>`_
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by Hastie et al.
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.. _omp:

‎doc/whats_new/upcoming_changes/sklearn.linear_model/31387.enhancement.rst

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- Fitting :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet` with
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`fit_intercept=True` is faster for sparse input `X` because an unnecessary
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re-computation of the sum of residuals is avoided.
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By :user:`Christian Lorentzen <lorentzenchr>`

‎doc/whats_new/upcoming_changes/sklearn.metrics/30886.fix.rst

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- Additional `sample_weight` checking has been added to
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:func:`metrics.mean_absolute_error`,
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:func:`metrics.mean_pinball_loss`,
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:func:`metrics.mean_absolute_percentage_error`,
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:func:`metrics.mean_squared_error`,
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:func:`metrics.root_mean_squared_error`,
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:func:`metrics.mean_squared_log_error`,
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:func:`metrics.root_mean_squared_log_error`,
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:func:`metrics.explained_variance_score`,
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:func:`metrics.r2_score`,
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:func:`metrics.mean_tweedie_deviance`,
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:func:`metrics.mean_poisson_deviance`,
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:func:`metrics.mean_gamma_deviance` and
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:func:`metrics.d2_tweedie_score`.
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`sample_weight` can only be 1D, consistent to `y_true` and `y_pred` in length
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or a scalar.
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By :user:`Lucy Liu <lucyleeow>`.

‎examples/miscellaneous/plot_outlier_detection_bench.py

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@@ -355,8 +355,7 @@ def fit_predict(estimator, X):
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ax=ax,
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plot_chance_level=(model_idx == len(n_neighbors_list) - 1),
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chance_level_kw={"linestyle": (0, (1, 10))},
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linestyle=linestyle,
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linewidth=2,
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curve_kwargs=dict(linestyle=linestyle, linewidth=2),
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)
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_ = ax.set_title("RobustScaler with varying n_neighbors\non forestcover dataset")
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ax=ax,
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plot_chance_level=(model_idx == len(preprocessor_list) - 1),
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chance_level_kw={"linestyle": (0, (1, 10))},
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linestyle=linestyle,
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linewidth=2,
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curve_kwargs=dict(linestyle=linestyle, linewidth=2),
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)
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_ = ax.set_title("Fixed n_neighbors with varying preprocessing\non forestcover dataset")
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ax=ax,
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plot_chance_level=(model_idx == len(preprocessor_list) - 1),
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chance_level_kw={"linestyle": (0, (1, 10))},
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linestyle=linestyle,
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linewidth=2,
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curve_kwargs=dict(linestyle=linestyle, linewidth=2),
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)
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ax.set_title(
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"Fixed n_neighbors with varying preprocessing\non cardiotocography dataset"

‎examples/miscellaneous/plot_roc_curve_visualization_api.py

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rfc = RandomForestClassifier(n_estimators=10, random_state=42)
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rfc.fit(X_train, y_train)
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ax = plt.gca()
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rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
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svc_disp.plot(ax=ax, alpha=0.8)
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rfc_disp = RocCurveDisplay.from_estimator(
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rfc, X_test, y_test, ax=ax, curve_kwargs=dict(alpha=0.8)
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)
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svc_disp.plot(ax=ax, curve_kwargs=dict(alpha=0.8))
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plt.show()

‎examples/model_selection/plot_cost_sensitive_learning.py

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X_test,
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y_test,
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pos_label=pos_label,
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linestyle=linestyle,
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color=color,
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curve_kwargs=dict(linestyle=linestyle, color=color),
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ax=axs[1],
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name=name,
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plot_chance_level=idx == 1,

‎examples/model_selection/plot_det.py

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)
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clf.fit(X_train, y_train)
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RocCurveDisplay.from_estimator(
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clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle
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clf,
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X_test,
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y_test,
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ax=ax_roc,
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name=name,
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curve_kwargs=dict(color=color, linestyle=linestyle),
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)
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DetCurveDisplay.from_estimator(
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clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle

‎examples/model_selection/plot_roc.py

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y_onehot_test[:, class_id],
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y_score[:, class_id],
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name=f"{class_of_interest} vs the rest",
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color="darkorange",
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curve_kwargs=dict(color="darkorange"),
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plot_chance_level=True,
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despine=True,
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)
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y_onehot_test.ravel(),
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y_score.ravel(),
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name="micro-average OvR",
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color="darkorange",
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curve_kwargs=dict(color="darkorange"),
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plot_chance_level=True,
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despine=True,
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)
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y_onehot_test[:, class_id],
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y_score[:, class_id],
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name=f"ROC curve for {target_names[class_id]}",
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color=color,
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curve_kwargs=dict(color=color),
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ax=ax,
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plot_chance_level=(class_id == 2),
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despine=True,

‎examples/model_selection/plot_roc_crossval.py

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X[test],
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y[test],
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name=f"ROC fold {fold}",
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alpha=0.3,
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lw=1,
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curve_kwargs=dict(alpha=0.3, lw=1),
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ax=ax,
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plot_chance_level=(fold == n_splits - 1),
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)

‎sklearn/cluster/_bicluster.py

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array([0, 0], dtype=int32)
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>>> clustering
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SpectralCoclustering(n_clusters=2, random_state=0)
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For a more detailed example, see the following:
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:ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py`.
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"""
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_parameter_constraints: dict = {

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