DOC fix grammatical errors in AdaBoost docstrings (scikit-learn#34355)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

Author: vashita-pandey

sklearn/ensemble/_weight_boosting.py

@@ -640,8 +640,8 @@ def decision_function(self, X):
         -------
         score : ndarray of shape of (n_samples, k)
             The decision function of the input samples. The order of
-            outputs is the same as that of the :term:`classes_` attribute.
-            Binary classification is a special cases with ``k == 1``,
+            outputs is the same as in the :term:`classes_` attribute.
+            Binary classification is a special case with ``k == 1``,
             otherwise ``k==n_classes``. For binary classification,
             values closer to -1 or 1 mean more like the first or second
             class in ``classes_``, respectively.
@@ -686,8 +686,8 @@ def staged_decision_function(self, X):
         ------
         score : generator of ndarray of shape (n_samples, k)
             The decision function of the input samples. The order of
-            outputs is the same of that of the :term:`classes_` attribute.
-            Binary classification is a special cases with ``k == 1``,
+            outputs is the same as in the :term:`classes_` attribute.
+            Binary classification is a special case with ``k == 1``,
             otherwise ``k==n_classes``. For binary classification,
             values closer to -1 or 1 mean more like the first or second
             class in ``classes_``, respectively.
@@ -757,7 +757,7 @@ def predict_proba(self, X):
         -------
         p : ndarray of shape (n_samples, n_classes)
             The class probabilities of the input samples. The order of
-            outputs is the same of that of the :term:`classes_` attribute.
+            outputs is the same as in the :term:`classes_` attribute.
         """
         check_is_fitted(self)
         n_classes = self.n_classes_
@@ -790,7 +790,7 @@ def staged_predict_proba(self, X):
         ------
         p : generator of ndarray of shape (n_samples,)
             The class probabilities of the input samples. The order of
-            outputs is the same of that of the :term:`classes_` attribute.
+            outputs is the same as in the :term:`classes_` attribute.
         """
 
         n_classes = self.n_classes_
@@ -815,21 +815,21 @@ def predict_log_proba(self, X):
         -------
         p : ndarray of shape (n_samples, n_classes)
             The class probabilities of the input samples. The order of
-            outputs is the same of that of the :term:`classes_` attribute.
+            outputs is the same as in the :term:`classes_` attribute.
         """
         return np.log(self.predict_proba(X))
 
 
 class AdaBoostRegressor(_RoutingNotSupportedMixin, RegressorMixin, BaseWeightBoosting):
     """An AdaBoost regressor.
 
-    An AdaBoost [1] regressor is a meta-estimator that begins by fitting a
+    An AdaBoost [1]_ regressor is a meta-estimator that begins by fitting a
     regressor on the original dataset and then fits additional copies of the
     regressor on the same dataset but where the weights of instances are
     adjusted according to the error of the current prediction. As such,
     subsequent regressors focus more on difficult cases.
 
-    This class implements the algorithm known as AdaBoost.R2 [2].
+    This class implements the algorithm known as AdaBoost.R2 [2]_.
 
     Read more in the :ref:`User Guide <adaboost>`.
 
@@ -917,7 +917,8 @@ class AdaBoostRegressor(_RoutingNotSupportedMixin, RegressorMixin, BaseWeightBoo
     .. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
            on-Line Learning and an Application to Boosting", 1995.
 
-    .. [2] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
+    .. [2] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
+           <https://dl.acm.org/doi/10.5555/645526.657132>`_
 
     Examples
     --------