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On the Use of Random Forest for Two-Sample Testing
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Following the line of classification-based two-sample testing, tests based on the Random Forest classifier are proposed. The developed tests are easy to use, require almost no tuning, and are applicable for any distribution on $\mathbb{R}^d$. Furthermore, the built-in variable importance measure of the Random Forest gives potential insights into which variables make out the difference in distribution. An asymptotic power analysis for the proposed tests is developed. Finally, two real-world applications illustrate the usefulness of the introduced methodology. To simplify the use of the method, the R-package "hypoRF" is provided.
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Cited by 1 Pith paper
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An Empirical Comparison of Methods for Quantifying the Similarity of Numeric Datasets
An empirical benchmark ranks 36 dataset similarity methods for continuous data and recommends decision rules plus small combinations that match top performance in 90-95% of tested two- and multi-sample scenarios.
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