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[DOC] Remove old error functions from docstrings #21314

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Merged
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Oct 14, 2021
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[DOC] Remove old error functions from docstrings #21314

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e044711
[DOC] Remove mae-mse from docstrings
luxedo Oct 12, 2021
3f52b5c
[DOC] Remove ls lad least_squares from docstrings
luxedo Oct 12, 2021
b2aad98
[DOC] fix missing end slash
luxedo Oct 12, 2021
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3 changes: 1 addition & 2 deletions sklearn/ensemble/_forest.py
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Expand Up @@ -2061,8 +2061,7 @@ class ExtraTreesRegressor(ForestRegressor):
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.

criterion : {"squared_error", "mse", "absolute_error", "mae"}, \
default="squared_error"
criterion : {"squared_error", "absolute_error"}, default="squared_error"
The function to measure the quality of a split. Supported criteria
are "squared_error" for the mean squared error, which is equal to
variance reduction as feature selection criterion, and "absolute_error"
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4 changes: 2 additions & 2 deletions sklearn/ensemble/_gb.py
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Expand Up @@ -1474,8 +1474,8 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):

Parameters
----------
loss : {'squared_error', 'ls', 'absolute_error', 'lad', 'huber', \
'quantile'}, default='squared_error'
loss : {'squared_error', 'absolute_error', 'huber', 'quantile'}, \
default='squared_error'
Loss function to be optimized. 'squared_error' refers to the squared
error for regression. 'absolute_error' refers to the absolute error of
regression and is a robust loss function. 'huber' is a
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6 changes: 3 additions & 3 deletions sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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Expand Up @@ -1021,10 +1021,10 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):

Parameters
----------
loss : {'squared_error', 'least_squares', 'absolute_error', \
'least_absolute_deviation', 'poisson'}, default='squared_error'
loss : {'squared_error', 'absolute_error', 'poisson'}, \
default='squared_error'
The loss function to use in the boosting process. Note that the
"least squares" and "poisson" losses actually implement
"squared error" and "poisson" losses actually implement
"half least squares loss" and "half poisson deviance" to simplify the
computation of the gradient. Furthermore, "poisson" loss internally
uses a log-link and requires ``y >= 0``.
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7 changes: 3 additions & 4 deletions sklearn/tree/_classes.py
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Expand Up @@ -1038,8 +1038,8 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):

Parameters
----------
criterion : {"squared_error", "mse", "friedman_mse", "absolute_error", \
"mae", "poisson"}, default="squared_error"
criterion : {"squared_error", "friedman_mse", "absolute_error", \
"poisson"}, default="squared_error"
The function to measure the quality of a split. Supported criteria
are "squared_error" for the mean squared error, which is equal to
variance reduction as feature selection criterion and minimizes the L2
Expand Down Expand Up @@ -1630,8 +1630,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):

Parameters
----------
criterion : {"squared_error", "mse", "friedman_mse", "mae"}, \
default="squared_error"
criterion : {"squared_error", "friedman_mse"}, default="squared_error"
The function to measure the quality of a split. Supported criteria
are "squared_error" for the mean squared error, which is equal to
variance reduction as feature selection criterion and "mae" for the
Expand Down