A Unified Framework for Random Forest Prediction Error Estimation

Abstract

We introduce a unified framework for random forest prediction error estimation based on a novel estimator of the conditional prediction error distribution function. Our framework enables simple plug-in estimation of key prediction uncertainty metrics, including conditional mean squared prediction errors, conditional biases, and conditional quantiles, for random forests and many variants. Our approach is especially well-adapted for prediction interval estimation; we show via simulations that our proposed prediction intervals are competitive with, and in some settings outperform, existing methods. To establish theoretical grounding for our framework, we prove pointwise uniform consistency of a more stringent version of our estimator of the conditional prediction error distribution function. The estimators introduced here are implemented in the R package forestError.

arXiv comment41 pages, 8 figures, 6 tables

References in corpus (5)

• Analysis of a Random Forests Model
• Consistency of random forests
• Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
• Adaptive Concentration of Regression Trees, with Application to Random Forests
• Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate