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DOC Ensures that AdaBoostRegressor passes numpydoc validation #20400

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Jun 28, 2021
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DOC Ensures that AdaBoostRegressor passes numpydoc validation #20400

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6457799
Adresses #20308 AdaBoostRegressor
gitdoluquita Jun 26, 2021
8753b3e
Merge branch 'main' of scikit
gitdoluquita Jun 26, 2021
1c3ef7c
Fixing default of learning rate
gitdoluquita Jun 26, 2021
0ea4d9e
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
gitdoluquita Jun 27, 2021
888be25
Changing AdaBoostClassifier description
gitdoluquita Jun 27, 2021
7ee5196
Update sklearn/ensemble/_weight_boosting.py
glemaitre Jun 28, 2021
5a6d5a9
Merge branch 'main' into 20308/AdaBoostRegressor
glemaitre Jun 28, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,6 @@

# List of modules ignored when checking for numpydoc validation.
DOCSTRING_IGNORE_LIST = [
"AdaBoostRegressor",
"AdditiveChi2Sampler",
"AffinityPropagation",
"AgglomerativeClustering",
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29 changes: 15 additions & 14 deletions sklearn/ensemble/_weight_boosting.py
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Expand Up @@ -924,7 +924,7 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.

learning_rate : float, default=1.
learning_rate : float, default=1.0
Weight applied to each classifier at each boosting iteration. A higher
learning rate increases the contribution of each classifier. There is
a trade-off between the `learning_rate` and `n_estimators` parameters.
Expand Down Expand Up @@ -969,6 +969,19 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):

.. versionadded:: 0.24

See Also
--------
AdaBoostClassifier : An AdaBoost classifier.
GradientBoostingRegressor : Gradient Boosting Classification Tree.
sklearn.tree.DecisionTreeRegressor : A decision tree regressor.

References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.

.. [2] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.

Examples
--------
>>> from sklearn.ensemble import AdaBoostRegressor
Expand All @@ -982,19 +995,6 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
array([4.7972...])
>>> regr.score(X, y)
0.9771...

See Also
--------
AdaBoostClassifier, GradientBoostingRegressor,
sklearn.tree.DecisionTreeRegressor

References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.

.. [2] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.

"""

def __init__(
Expand Down Expand Up @@ -1036,6 +1036,7 @@ def fit(self, X, y, sample_weight=None):
Returns
-------
self : object
Fitted AdaBoostRegressor estimator.
"""
# Check loss
if self.loss not in ("linear", "square", "exponential"):
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