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DOC improve learning-rate AdaBoost estimator #19919

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Merged
merged 3 commits into from
Apr 20, 2021
Merged

DOC improve learning-rate AdaBoost estimator #19919

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9aedc51
DOC: Adaboost Learning Rate Documentation
bharatr21 Apr 18, 2021
e79b7fc
DOC: Add changes to Regressor
bharatr21 Apr 19, 2021
6f46d5b
DOC: Rephrase according to suggestions
bharatr21 Apr 19, 2021
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12 changes: 6 additions & 6 deletions 12 sklearn/ensemble/_weight_boosting.py
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Expand Up @@ -313,9 +313,9 @@ class AdaBoostClassifier(ClassifierMixin, BaseWeightBoosting):
In case of perfect fit, the learning procedure is stopped early.

learning_rate : float, default=1.
Learning rate shrinks the contribution of each classifier by
``learning_rate``. There is a trade-off between ``learning_rate`` and
``n_estimators``.
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.

algorithm : {'SAMME', 'SAMME.R'}, default='SAMME.R'
If 'SAMME.R' then use the SAMME.R real boosting algorithm.
Expand Down Expand Up @@ -898,9 +898,9 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
In case of perfect fit, the learning procedure is stopped early.

learning_rate : float, default=1.
Learning rate shrinks the contribution of each regressor by
``learning_rate``. There is a trade-off between ``learning_rate`` and
``n_estimators``.
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.

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