rasbt
diff --git a/‎sklearn/ensemble/_weight_boosting.py
Lines changed: 36 additions & 21 deletions b/‎sklearn/ensemble/_weight_boosting.py
Lines changed: 36 additions & 21 deletions
@@ -223,7 +223,7 @@ def staged_score(self, X, y, sample_weight=None):
         sample_weight : array-like of shape (n_samples,), default=None
             Sample weights.
 
-        Returns
+        Yields
         -------
         z : float
         """
@@ -242,7 +242,8 @@ def feature_importances_(self):
 
         Returns
         -------
-        feature_importances_ : array, shape = [n_features]
+        feature_importances_ : ndarray of shape (n_features,)
+            The feature importances.
         """
         if self.estimators_ is None or len(self.estimators_) == 0:
             raise ValueError("Estimator not fitted, "
@@ -302,9 +303,9 @@ class AdaBoostClassifier(ClassifierMixin, BaseWeightBoosting):
         The base estimator from which the boosted ensemble is built.
         Support for sample weighting is required, as well as proper
         ``classes_`` and ``n_classes_`` attributes. If ``None``, then
-        the base estimator is ``DecisionTreeClassifier(max_depth=1)``
+        the base estimator is ``DecisionTreeClassifier(max_depth=1)``.
 
-    n_estimators : integer, optional (default=50)
+    n_estimators : int, optional (default=50)
         The maximum number of estimators at which boosting is terminated.
         In case of perfect fit, the learning procedure is stopped early.
 
@@ -350,6 +351,32 @@ class AdaBoostClassifier(ClassifierMixin, BaseWeightBoosting):
     feature_importances_ : ndarray of shape (n_features,)
         The feature importances if supported by the ``base_estimator``.
 
+    See Also
+    --------
+    AdaBoostRegressor
+        An AdaBoost regressor 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.
+
+    GradientBoostingClassifier
+        GB builds an additive model in a forward stage-wise fashion. Regression
+        trees are fit on the negative gradient of the binomial or multinomial
+        deviance loss function. Binary classification is a special case where
+        only a single regression tree is induced.
+
+    sklearn.tree.DecisionTreeClassifier
+        A non-parametric supervised learning method used for classification.
+        Creates a model that predicts the value of a target variable by
+        learning simple decision rules inferred from the data features.
+
+    References
+    ----------
+    .. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
+           on-Line Learning and an Application to Boosting", 1995.
+
+    .. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
+
     Examples
     --------
     >>> from sklearn.ensemble import AdaBoostClassifier
@@ -366,19 +393,6 @@ class AdaBoostClassifier(ClassifierMixin, BaseWeightBoosting):
     array([1])
     >>> clf.score(X, y)
     0.983...
-
-    See also
-    --------
-    AdaBoostRegressor, GradientBoostingClassifier,
-    sklearn.tree.DecisionTreeClassifier
-
-    References
-    ----------
-    .. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
-           on-Line Learning and an Application to Boosting", 1995.
-
-    .. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
-
     """
     def __init__(self,
                  base_estimator=None,
@@ -414,6 +428,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            A fitted estimator.
         """
         # Check that algorithm is supported
         if self.algorithm not in ('SAMME', 'SAMME.R'):
@@ -632,7 +647,7 @@ def staged_predict(self, X):
             The input samples. Sparse matrix can be CSC, CSR, COO,
             DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 
-        Returns
+        Yields
         -------
         y : generator of array, shape = [n_samples]
             The predicted classes.
@@ -703,7 +718,7 @@ def staged_decision_function(self, X):
             The training input samples. Sparse matrix can be CSC, CSR, COO,
             DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 
-        Returns
+        Yields
         -------
         score : generator of array, shape = [n_samples, k]
             The decision function of the input samples. The order of
@@ -811,7 +826,7 @@ def staged_predict_proba(self, X):
             The training input samples. Sparse matrix can be CSC, CSR, COO,
             DOK, or LIL. COO, DOK, and LIL are converted to CSR.
 
-        Returns
+        Yields
         -------
         p : generator of array, shape = [n_samples]
             The class probabilities of the input samples. The order of
@@ -1130,7 +1145,7 @@ def staged_predict(self, X):
         X : {array-like, sparse matrix} of shape (n_samples, n_features)
             The training input samples.
 
-        Returns
+        Yields
         -------
         y : generator of array, shape = [n_samples]
             The predicted regression values.