Ensembles of Probabilistic Regression Trees
This work addresses regression tasks in machine learning, offering an incremental improvement by extending probabilistic trees to ensembles.
The paper tackles the problem of improving regression accuracy by proposing ensemble versions of probabilistic regression trees, which provide smooth approximations and are proven consistent, with experimental comparisons showing competitive performance against state-of-the-art methods.
Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applicationsand research studies. In this paper, we study ensemble versions of probabilisticregression trees that provide smooth approximations of the objective function by assigningeach observation to each region with respect to a probability distribution. We prove thatthe ensemble versions of probabilistic regression trees considered are consistent, and experimentallystudy their bias-variance trade-off and compare them with the state-of-the-art interms of performance prediction.