Feature Engineering for Predictive Modeling using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based framework to automate feature engineering by systematically exploring transformation options, reducing reliance on human intuition and trial-and-error, and improving predictive model performance.

Contribution

The paper proposes a novel reinforcement learning approach to automate feature engineering through performance-driven exploration of transformation graphs.

Findings

Efficient exploration strategy derived from reinforcement learning.

Automated feature engineering reduces manual effort and trial-and-error.

Framework improves predictive modeling accuracy.

Abstract

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.