  We propose a novel multivariate nonparametric multiple change point detection
method using classifiers. We construct a classifier log-likelihood ratio that
uses class probability predictions to compare different change point
configurations. We propose a computationally feasible search method that is
particularly well suited for random forests, denoted by changeforest. However,
the method can be paired with any classifier that yields class probability
predictions, which we illustrate by also using a k-nearest neighbor classifier.
We prove that it consistently locates change points in single change point
settings when paired with a consistent classifier. Our proposed method
changeforest achieves improved empirical performance in an extensive simulation
study compared to existing multivariate nonparametric change point detection
methods. An efficient implementation of our method is made available for R,
Python, and Rust users in the changeforest software package.

Methodology;Machine Learning