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Abstract:
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Random forests, a data mining technique which uses multiple classification or regression trees, is a popular algorithm used for prediction. Inference and goodness-of-fit assessment, however, may require an estimator of variability. When a modified random forest algorithm is used to model mixed-effects data, an estimator of the vector of variance components is also required for prediction. We propose two estimators of the vector of variance components for random forest regression that take advantage of byproducts of the algorithm. The first estimator is based on the residual sum of squares from a random forest fit and uses a bootstrap bias correction. The second estimator is a difference-based estimator that uses proximity measures as weights. The estimators are evaluated through Monte Carlo simulations.
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