DOC improve learning-rate AdaBoost estimator #19919
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glemaitre
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Apr 19, 2021
Updated |
glemaitre
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Apr 19, 2021
sklearn/ensemble/_weight_boosting.py
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| @@ -314,8 +314,9 @@ class AdaBoostClassifier(ClassifierMixin, BaseWeightBoosting): | |||
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| 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``. | |||
| ``learning_rate`` whereas a higher learning rate increases the | |||
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actually I don't think that whereas is the right conjunction.
Can you just rephrase with
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.
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Thanks @Bharat123rox |
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Reference Issues/PRs
#19521
What does this implement/fix? Explain your changes.
Fixes #19521 by updating documentation
Any other comments?
Please review for verbosity