arXiv Machine Learning
Jun 24
Experiments with Optimal Model Trees
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
Twoblock clustering trees with coskewness-based dimension reduction: recovering piecewise multivariate linear regimes
The paper introduces the twoblock clustering tree (TBTREE), a deterministic regression tree designed for multivariate responses that uses local multivariate linear models as leaves and incorporates dense or sparse twoblock dimension reduction both in the leaf models and impurity calculation. TBTREE aims to provide an interpretable, regime‑aligned piecewise‑linear description of data while maintaining predictive performance through a split/leaf decoupling strategy. Additionally, the authors propose a coskewness‑based estimator for the twoblock reduced space to identify non‑normal clusters, demonstrating the method’s ability to recover piecewise linear regimes in simulations and to model complex nonlinear dependencies in real‑world datasets, achieving performance comparable to black‑box techniques such as random forests.
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