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diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
index 096770c1d..ebde568f5 100644
--- a/skopt/learning/forest.py
+++ b/skopt/learning/forest.py
@@ -27,7 +27,7 @@ def _return_std(X, trees, predictions, min_variance):
-------
std : array-like, shape=(n_samples,)
Standard deviation of `y` at `X`. If criterion
- is set to "mse", then `std[i] ~= std(y | X[i])`.
+ is set to "squared_error", then `std[i] ~= std(y | X[i])`.
"""
# This derives std(y | x) as described in 4.3.2 of arXiv:1211.0906
@@ -61,9 +61,9 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
n_estimators : integer, optional (default=10)
The number of trees in the forest.
- criterion : string, optional (default="mse")
+ criterion : string, optional (default="squared_error")
The function to measure the quality of a split. Supported criteria
- are "mse" for the mean squared error, which is equal to variance
+ are "squared_error" for the mean squared error, which is equal to variance
reduction as feature selection criterion, and "mae" for the mean
absolute error.
@@ -194,7 +194,7 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
"""
- def __init__(self, n_estimators=10, criterion='mse', max_depth=None,
+ def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
min_samples_split=2, min_samples_leaf=1,
min_weight_fraction_leaf=0.0, max_features='auto',
max_leaf_nodes=None, min_impurity_decrease=0.,
@@ -228,20 +228,20 @@ def predict(self, X, return_std=False):
Returns
-------
predictions : array-like of shape = (n_samples,)
- Predicted values for X. If criterion is set to "mse",
+ Predicted values for X. If criterion is set to "squared_error",
then `predictions[i] ~= mean(y | X[i])`.
std : array-like of shape=(n_samples,)
Standard deviation of `y` at `X`. If criterion
- is set to "mse", then `std[i] ~= std(y | X[i])`.
+ is set to "squared_error", then `std[i] ~= std(y | X[i])`.
"""
mean = super(RandomForestRegressor, self).predict(X)
if return_std:
- if self.criterion != "mse":
+ if self.criterion != "squared_error":
raise ValueError(
- "Expected impurity to be 'mse', got %s instead"
+ "Expected impurity to be 'squared_error', got %s instead"
% self.criterion)
std = _return_std(X, self.estimators_, mean, self.min_variance)
return mean, std
@@ -257,9 +257,9 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
n_estimators : integer, optional (default=10)
The number of trees in the forest.
- criterion : string, optional (default="mse")
+ criterion : string, optional (default="squared_error")
The function to measure the quality of a split. Supported criteria
- are "mse" for the mean squared error, which is equal to variance
+ are "squared_error" for the mean squared error, which is equal to variance
reduction as feature selection criterion, and "mae" for the mean
absolute error.
@@ -390,7 +390,7 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
"""
- def __init__(self, n_estimators=10, criterion='mse', max_depth=None,
+ def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
min_samples_split=2, min_samples_leaf=1,
min_weight_fraction_leaf=0.0, max_features='auto',
max_leaf_nodes=None, min_impurity_decrease=0.,
@@ -425,19 +425,19 @@ def predict(self, X, return_std=False):
Returns
-------
predictions : array-like of shape=(n_samples,)
- Predicted values for X. If criterion is set to "mse",
+ Predicted values for X. If criterion is set to "squared_error",
then `predictions[i] ~= mean(y | X[i])`.
std : array-like of shape=(n_samples,)
Standard deviation of `y` at `X`. If criterion
- is set to "mse", then `std[i] ~= std(y | X[i])`.
+ is set to "squared_error", then `std[i] ~= std(y | X[i])`.
"""
mean = super(ExtraTreesRegressor, self).predict(X)
if return_std:
- if self.criterion != "mse":
+ if self.criterion != "squared_error":
raise ValueError(
- "Expected impurity to be 'mse', got %s instead"
+ "Expected impurity to be 'squared_error', got %s instead"
% self.criterion)
std = _return_std(X, self.estimators_, mean, self.min_variance)
return mean, std
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