FACT: High-Dimensional Random Forests Inference
This work addresses the interpretability issue in random forests for researchers and practitioners by offering a bias-resistant method, though it is incremental as it builds on recent consistency developments.
The paper tackles the problem of biased feature importance measures in random forests by proposing a hypothesis testing framework called FACT, which provides theoretically justified feature importance tests with controlled type I error and improved power, as demonstrated in simulations and an economic forecasting application.
Quantifying the usefulness of individual features in random forests learning can greatly enhance its interpretability. Existing studies have shown that some popularly used feature importance measures for random forests suffer from the bias issue. In addition, there lack comprehensive size and power analyses for most of these existing methods. In this paper, we approach the problem via hypothesis testing, and suggest a framework of the self-normalized feature-residual correlation test (FACT) for evaluating the significance of a given feature in the random forests model with bias-resistance property, where our null hypothesis concerns whether the feature is conditionally independent of the response given all other features. Such an endeavor on random forests inference is empowered by some recent developments on high-dimensional random forests consistency. Under a fairly general high-dimensional nonparametric model setting with dependent features, we formally establish that FACT can provide theoretically justified feature importance test with controlled type I error and enjoy appealing power property. The theoretical results and finite-sample advantages of the newly suggested method are illustrated with several simulation examples and an economic forecasting application.