DOC Update documentation of weight boosting estimators w/ranges
@@ -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.
@@ -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
