Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests

Understand

This work develops formal statistical inference procedures for machine learning ensemble methods.

• Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but fail to provide a framework in which distributional results can be easily determined.
• Instead of aggregating full bootstrap samples, we consider predicting by averaging over trees built on subsamples of the training set and demonstrate that the resulting estimator takes the form of a U-statistic.
• As such, predictions for individual feature vectors are asymptotically normal, allowing for confidence intervals to accompany predictions.