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.
@article{arxiv.1709.07150,
title = {Feature Engineering for Predictive Modeling using Reinforcement Learning},
author = {Udayan Khurana and Horst Samulowitz and Deepak Turaga},
journal= {arXiv preprint arXiv:1709.07150},
year = {2017}
}