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Set reg_lambda=1e-5 for scikit-learn-like random forest classes. (#4558)

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Andy Adinets 2019-06-21 22:02:13 +02:00 committed by Rory Mitchell
parent 30e1cb4e9e
commit 9fa29ad753
1 changed files with 2 additions and 2 deletions
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python-package/xgboost/sklearn.py
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@ -892,7 +892,7 @@ class XGBRFClassifier(XGBClassifier):
verbosity=1, silent=None,
objective="binary:logistic", n_jobs=1, nthread=None, gamma=0,
min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1,
colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1,
colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1e-5,
scale_pos_weight=1, base_score=0.5, random_state=0, seed=None,
missing=None, **kwargs):
super(XGBRFClassifier, self).__init__(
@ -931,7 +931,7 @@ class XGBRFRegressor(XGBRegressor):
verbosity=1, silent=None,
objective="reg:squarederror", n_jobs=1, nthread=None, gamma=0,
min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1,
colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1,
colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1e-5,
scale_pos_weight=1, base_score=0.5, random_state=0, seed=None,
missing=None, **kwargs):
super(XGBRFRegressor, self).__init__(