Parallel Large Scale Feature Selection for Logistic Regression
Sameer Kumar Singh, Jeremy Kubica, Scott Larsen, Daria Sorokina · 2009
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.