Merge pull request scikit-learn#6944 from proinsias/proinsias-rfc-doc-defaults

DOC Add missing parameter defaults for some ensembles

Author: glouppe

sklearn/ensemble/forest.py

@@ -808,7 +808,7 @@ class RandomForestClassifier(ForestClassifier):
     bootstrap : boolean, optional (default=True)
         Whether bootstrap samples are used when building trees.
 
-    oob_score : bool
+    oob_score : bool (default=False)
         Whether to use out-of-bag samples to estimate
         the generalization accuracy.
 
@@ -830,7 +830,8 @@ class RandomForestClassifier(ForestClassifier):
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest.
 
-    class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional
+    class_weight : dict, list of dicts, "balanced",
+        "balanced_subsample" or None, optional (default=None)
         Weights associated with classes in the form ``{class_label: weight}``.
         If not given, all classes are supposed to have weight one. For
         multi-output problems, a list of dicts can be provided in the same
@@ -1182,7 +1183,7 @@ class ExtraTreesClassifier(ForestClassifier):
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest.
 
-    class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional
+    class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional (default=None)
         Weights associated with classes in the form ``{class_label: weight}``.
         If not given, all classes are supposed to have weight one. For
         multi-output problems, a list of dicts can be provided in the same
@@ -1460,10 +1461,10 @@ class RandomTreesEmbedding(BaseForest):
 
     Parameters
     ----------
-    n_estimators : int
+    n_estimators : integer, optional (default=10)
         Number of trees in the forest.
 
-    max_depth : int
+    max_depth : integer, optional (default=5)
         The maximum depth of each tree. If None, then nodes are expanded until
         all leaves are pure or until all leaves contain less than
         min_samples_split samples.