Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple objectives: improving the prediction accuracy by eliminating irrelevant features, accelerating the model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnosis capability. However, selecting an optimal feature subset from a large feature space is considered as an NPcomplete problem. The mRMR (Minimum Redundancy and Maximum Relevance) feature selection framework solves this problem by selecting the relevant features while controlling for the redundancy within the selected features. This paper describes the approach to extend, evaluate, and implement the mRMR feature selection methods for classification problem in a marketing machine learning platform at Uber that automates creation and deployment of targeting and personalization models at scale. This study first extends the existing mRMR methods by introducing a nonlinear feature redundancy measure and a modelbased feature relevance measure. Then an extensive empirical evaluation is performed for eight different feature selection methods, using one synthetic dataset and three realworld marketing datasets at Uber to cover different use cases. Based on the empirical results, the selected mRMR method is implemented in production for the marketing machine learning platform. A description of the production implementation is provided and an online experiment deployed through the platform is discussed.

Hierarchical Classification and Regression with Feature Selection Previous studies proposed different hierarchical estimation approaches for solving certain specific domain problems. They usually combine two or more estimation models in a hierarchical fashion. Therefore, in our previous work 2, we proposed a hierarchical approach for generic purposes, the Hierarchical Classification and Regression (HCR), that incorporates classification and estimation techniques. The HCR 2 approach significantly outperformed three benchmark flat estimation models. Having seen the potential of the proposed HCR as a generic hierarchical regression scheme, we propose to further improve the HCR by introducing feature selection (FS) techniques to the HCR. In order to thoroughly investigate the effect of FS on the HCR, we examine different numbers of attributes remained after feature selection with respect to datasets of various sizes. The results showed that the HCR with linear regression performed significantly better than the other HCRs while feature selection helped lower the RMSE slightly with only 50% of the original features.