  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.
