- Weichen Shen,firstname.lastname@example.org
-  Gai K, Zhu X, Li H, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction[J]. arXiv preprint arXiv:1704.05194, 2017.(https://arxiv.org/abs/1704.05194)
MLR(region_feature_columns, base_feature_columns=None, region_num=4, l2_reg_linear=1e-05, seed=1024, task='binary', bias_feature_columns=None)¶
Instantiates the Mixed Logistic Regression/Piece-wise Linear Model.
- region_feature_columns – An iterable containing all the features used by region part of the model.
- base_feature_columns – An iterable containing all the features used by base part of the model.
- region_num – integer > 1,indicate the piece number
- l2_reg_linear – float. L2 regularizer strength applied to weight
- seed – integer ,to use as random seed.
- task – str,
"binary"for binary logloss or
"regression"for regression loss
- bias_feature_columns – An iterable containing all the features used by bias part of the model.
A Keras model instance.