Feature Engineering for Predictive Modeling using Reinforcement Learning
This addresses the high cost and inefficiency of manual feature engineering for data scientists and machine learning practitioners, offering an incremental improvement through automation.
The paper tackles the problem of automating feature engineering in predictive modeling, which is typically manual and costly, by introducing a reinforcement learning framework that explores transformation graphs, resulting in a highly efficient exploration strategy.
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.