deepctr.models.mlr module

Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] 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)
deepctr.models.mlr.MLR(region_feature_columns, base_feature_columns=None, region_num=4, l2_reg_linear=1e-05, init_std=0.0001, seed=1024, task='binary', bias_feature_columns=None)[source]

Instantiates the Mixed Logistic Regression/Piece-wise Linear Model.

Parameters:
  • 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
  • init_std – float,to use as the initialize std of embedding vector
  • 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.
Returns:

A Keras model instance.