In this paper we examine the problem of efficient feature evaluation for logistic regression on very large data sets. We present a new forward feature selection heuristic that ranks features by their estimated effect on the resulting model's performance. An approximate optimization, based on backfitting, provides a fast and accurate estimate of each new feature's coefficient in the logistic regression model. Further, the algorithm is highly scalable by parallelizing simultaneously over both features and records, allowing us to quickly evaluate billions of potential features even for very large data sets.
REF propose a framework for handling feature selection for logistic regression by developing a new forward feature selection heuristic that ranks features by their estimated effect on the resulting model's performance.
Parallel Large Scale Feature Selection for Logistic Regression
SDM
Computer Science