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Maximum Relevance and Minimum Redundancy Feature Selection Methods for a
  Marketing Machine Learning Platform

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform

15 August 2019
Zhenyu Zhao
Radhika Anand
Mallory Wang
ArXivPDFHTML

Papers citing "Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform"

3 / 3 papers shown
Title
Evaluation: from precision, recall and F-measure to ROC, informedness,
  markedness and correlation
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
D. Powers
173
5,279
0
11 Oct 2020
XGBoost: A Scalable Tree Boosting System
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
Carlos Guestrin
790
38,735
0
09 Mar 2016
The Randomized Dependence Coefficient
The Randomized Dependence Coefficient
David Lopez-Paz
Philipp Hennig
Bernhard Schölkopf
102
190
0
29 Apr 2013
1