From 378dd00791d34c190e3900d3fb7d8f202247455f Mon Sep 17 00:00:00 2001
From: Aline Ribeiro de Almeida <alinesudoku@gmail.com>
Date: Sat, 26 Jun 2021 22:50:20 +0200
Subject: [PATCH 1/2] Remove RandomForestRegressor from DOCSTRING_IGNORE_LIST

---
 maint_tools/test_docstrings.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
index 5944020883ab5..bfbeb545d818d 100644
--- a/maint_tools/test_docstrings.py
+++ b/maint_tools/test_docstrings.py
@@ -163,7 +163,6 @@
     "RadiusNeighborsRegressor",
     "RadiusNeighborsTransformer",
     "RandomForestClassifier",
-    "RandomForestRegressor",
     "RandomTreesEmbedding",
     "RandomizedSearchCV",
     "RegressorChain",

From 8944c1d9c81872753eaaa1c304ac651d5e69245c Mon Sep 17 00:00:00 2001
From: Aline Ribeiro de Almeida <alinesudoku@gmail.com>
Date: Sat, 26 Jun 2021 22:52:02 +0200
Subject: [PATCH 2/2] Fix numpydoc errors GL02, GL03, GL08, RT03, SA04, SS06 on
 RandomForestRegressor

---
 sklearn/base.py             |  3 +--
 sklearn/ensemble/_forest.py | 13 +++++++++++--
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/sklearn/base.py b/sklearn/base.py
index 0eb84f69299de..99a3c0e884efa 100644
--- a/sklearn/base.py
+++ b/sklearn/base.py
@@ -558,8 +558,7 @@ class RegressorMixin:
     _estimator_type = "regressor"
 
     def score(self, X, y, sample_weight=None):
-        """Return the coefficient of determination :math:`R^2` of the
-        prediction.
+        """Return the coefficient of determination :math:`R^2` of the prediction.
 
         The coefficient :math:`R^2` is defined as :math:`(1 - \\frac{u}{v})`,
         where :math:`u` is the residual sum of squares ``((y_true - y_pred)
diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
index 1b880d142cad6..6b08c36d62b64 100644
--- a/sklearn/ensemble/_forest.py
+++ b/sklearn/ensemble/_forest.py
@@ -276,7 +276,6 @@ def decision_path(self, X):
         n_nodes_ptr : ndarray of shape (n_estimators + 1,)
             The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
             gives the indicator value for the i-th estimator.
-
         """
         X = self._validate_X_predict(X)
         indicators = Parallel(
@@ -319,6 +318,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            Fitted estimator.
         """
         # Validate or convert input data
         if issparse(y):
@@ -615,6 +615,13 @@ def feature_importances_(self):
     )
     @property
     def n_features_(self):
+        """Number of features when fitting the estimator.
+
+        Returns
+        -------
+        n_features_in_ : int
+            The number of features when fitting the estimator.
+        """
         return self.n_features_in_
 
 
@@ -1595,7 +1602,9 @@ class RandomForestRegressor(ForestRegressor):
 
     See Also
     --------
-    DecisionTreeRegressor, ExtraTreesRegressor
+    sklearn.tree.DecisionTreeRegressor : A decision tree regressor.
+    sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized
+        tree regressors.
 
     Notes
     -----