8000 DOC Ensures that RandomForestRegressor passes numpydoc validation by alinealfa · Pull Request #20401 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that RandomForestRegressor passes numpydoc validation #20401

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Merged
merged 3 commits into from
Jun 29, 2021
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DOC Ensures that RandomForestRegressor passes numpydoc validation #20401

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378dd00
Remove RandomForestRegressor from DOCSTRING_IGNORE_LIST
alinealfa Jun 26, 2021
8944c1d
Fix numpydoc errors GL02, GL03, GL08, RT03, SA04, SS06 on RandomFores…
alinealfa Jun 26, 2021
e4a3c58
Merge branch 'main' into randomforestregressor_docstrings_pass_numpyd…
ogrisel Jun 29, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -150,7 +150,6 @@
"RadiusNeighborsRegressor",
"RadiusNeighborsTransformer",
"RandomForestClassifier",
"RandomForestRegressor",
"RandomTreesEmbedding",
"RandomizedSearchCV",
"RegressorChain",
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13 changes: 11 additions & 2 deletions sklearn/ensemble/_forest.py
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Expand Up @@ -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(
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -613,6 +613,13 @@ def feature_importances_(self):
)
@property
def n_features_(self):
"""Number of features when fitting the estimator.
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I open #20414 to check the issue with sphinx and as well to discuss property formatting. We can wait to see how do we want to format property.

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I think there is no problem with docstring.


Returns
-------
n_features_in_ : int
The number of features when fitting the estimator.
"""
return self.n_features_in_


Expand Down Expand Up @@ -1594,7 +1601,9 @@ class RandomForestRegressor(ForestRegressor):

See Also
--------
DecisionTreeRegressor, ExtraTreesRegressor
sklearn.tree.DecisionTreeRegressor : A decision tree regressor.
sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized
tree regressors.

Notes
-----
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