Reinforcement Learning

The performance of machine learning models depends heavily on the feature space and feature engineering. Although neural networks have made significant progress in learning latent feature spaces from data, compositional feature engineering through nested feature transformations can reduce model complexity and can be particularly desirable for interpretability. To address these shortcomings, we propose a reinforcement learning-based automatic feature engineering method, which we call Monte Carlo tree search Automatic Feature Engineering (mCAFE).