Cross-validated tree-based models for multi-target learning

Yehuda Nissenbaum, Amichai Painsky*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Multi-target learning (MTL) is a popular machine learning technique which considers simultaneous prediction of multiple targets. MTL schemes utilize a variety of methods, from traditional linear models to more contemporary deep neural networks. In this work we introduce a novel, highly interpretable, tree-based MTL scheme which exploits the correlation between the targets to obtain improved prediction accuracy. Our suggested scheme applies cross-validated splitting criterion to identify correlated targets at every node of the tree. This allows us to benefit from the correlation among the targets while avoiding overfitting. We demonstrate the performance of our proposed scheme in a variety of synthetic and real-world experiments, showing a significant improvement over alternative methods. An implementation of the proposed method is publicly available at the first author's webpage.

Original languageEnglish
Article number1302860
JournalFrontiers in Artificial Intelligence
StatePublished - 2024


FundersFunder number
Israel Science Foundation963/21


    • classification and regression trees
    • gradient boosting
    • multi-target learning
    • random forest
    • tree-based models


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