DOC Change classifier to regressor in docstring
@@ -925,8 +925,8 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
In case of perfect fit, the learning procedure is stopped early.
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
+ 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.
loss : {'linear', 'square', 'exponential'}, default='linear'
@@ -947,7 +947,7 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
base_estimator_ : estimator
The base estimator from which the ensemble is grown.
- estimators_ : list of classifiers
+ estimators_ : list of regressors
The collection of fitted sub-estimators.
estimator_weights_ : ndarray of floats
@@ -1168,7 +1168,7 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
"""Predict regression value for X.
The predicted regression value of an input sample is computed
- as the weighted median prediction of the classifiers in the ensemble.
+ as the weighted median prediction of the regressors in the ensemble.
Parameters
----------
@@ -1190,7 +1190,7 @@ class AdaBoostRegressor(RegressorMixin, BaseWeightBoosting):
"""Return staged predictions for X.
The predicted regression value of an input sample is computed
- as the weighted median prediction of the classifiers in the ensemble.
+ as the weighted median prediction of the regressors in the ensemble.
This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
