  Being able to successfully determine whether the testing samples has similar
distribution as the training samples is a fundamental question to address
before we can safely deploy most of the machine learning models into practice.
In this paper, we propose TOOD detection, a simple yet effective tree-based
out-of-distribution (TOOD) detection mechanism to determine if a set of unseen
samples will have similar distribution as of the training samples. The TOOD
detection mechanism is based on computing pairwise hamming distance of testing
samples' tree embeddings, which are obtained by fitting a tree-based ensemble
model through in-distribution training samples. Our approach is interpretable
and robust for its tree-based nature. Furthermore, our approach is efficient,
flexible to various machine learning tasks, and can be easily generalized to
unsupervised setting. Extensive experiments are conducted to show the proposed
method outperforms other state-of-the-art out-of-distribution detection methods
in distinguishing the in-distribution from out-of-distribution on various
tabular, image, and text data.
