scikit-learn
diff --git a/‎doc/whats_new/v1.1.rst
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diff --git a/‎sklearn/ensemble/_gb_losses.py
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diff --git a/‎sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py
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@@ -128,6 +128,28 @@ Changelog
   ``bootstrap=False`` and ``max_samples`` is not ``None``.
   :pr:`21295` :user:`Haoyin Xu <PSSF23>`.
 
+- |API| Changed the default of :func:`max_features` to 1.0 for
+  :class:`ensemble.RandomForestRegressor` and to `"sqrt"` for
+  :class:`ensemble.RandomForestClassifier`. Note that these give the same fit
+  results as before, but are much easier to understand. The old default value
+  `"auto"` has been deprecated and will be removed in version 1.3. The same
+  changes are also applied for :class:`ensemble.ExtraTreesRegressor` and
+  :class:`ensemble.ExtraTreesClassifier`.
+  :pr:`20803` by :user:`Brian Sun <bsun94>`.
+
+- |Fix| Solve a bug in :class:`ensemble.GradientBoostingClassifier` where the
+  exponential loss was computing the positive gradient instead of the
+  negative one.
+  :pr:`22050` by :user:`Guillaume Lemaitre <glemaitre>`.
+
+:mod:`sklearn.feature_extraction.text`
+......................................
+
+- |Fix| :class:`feature_extraction.text.TfidfVectorizer` now does not create
+  a :class:`feature_extraction.text.TfidfTransformer` at `__init__` as required
+  by our API.
+  :pr:`21832` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 :mod:`sklearn.impute`
 .....................
 
 
@@ -935,8 +935,8 @@ def negative_gradient(self, y, raw_predictions, **kargs):
             The raw predictions (i.e. values from the tree leaves) of the
             tree ensemble at iteration ``i - 1``.
         """
-        y_ = -(2.0 * y - 1.0)
-        return y_ * np.exp(y_ * raw_predictions.ravel())
+        y_ = 2.0 * y - 1.0
+        return y_ * np.exp(-y_ * raw_predictions.ravel())
 
     def _update_terminal_region(
         self,
 
@@ -320,3 +320,18 @@ def test_lad_equals_quantiles(seed, alpha):
         y_true, raw_predictions, sample_weight=weights, alpha=alpha
     )
     assert pbl_weighted_loss == approx(ql_weighted_loss)
+
+
+def test_exponential_loss():
+    """Check that we compute the negative gradient of the exponential loss.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/9666
+    """
+    loss = ExponentialLoss(n_classes=2)
+    y_true = np.array([0])
+    y_pred = np.array([0])
+    # we expect to have loss = exp(0) = 1
+    assert loss(y_true, y_pred) == pytest.approx(1)
+    # we expect to have negative gradient = -1 * (1 * exp(0)) = -1
+    assert_allclose(loss.negative_gradient(y_true, y_pred), -1)