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DOC Update documentation of weight boosting estimators w/ranges #22097

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Dec 31, 2021
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DOC Update documentation of weight boosting estimators w/ranges #22097

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4 changes: 4 additions & 0 deletions sklearn/ensemble/_weight_boosting.py
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Expand Up @@ -352,11 +352,13 @@ class AdaBoostClassifier(ClassifierMixin, BaseWeightBoosting):
n_estimators : int, default=50
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
Values should be in the range `[1, inf)`.

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.
Values should be in the range `(0.0, inf)`.

algorithm : {'SAMME', 'SAMME.R'}, default='SAMME.R'
If 'SAMME.R' then use the SAMME.R real boosting algorithm.
Expand Down Expand Up @@ -954,11 +956,13 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
n_estimators : int, default=50
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
Values should be in the range `[1, inf)`.

learning_rate : float, default=1.0
Weight applied to each regressor at each boosting iteration. A higher
learning rate increases the contribution of each regressor. There is
a trade-off between the `learning_rate` and `n_estimators` parameters.
Values should be in the range `(0.0, inf)`.

loss : {'linear', 'square', 'exponential'}, default='linear'
The loss function to use when updating the weights after each
Expand Down