Nespoli, Lorenzo and Medici, Vasco (2022) Multivariate Boosted Trees and Applications to Forecasting and Control. Journal of Machine Learning Research. ISSN 1533-7928
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Abstract
Gradient boosted trees are competition-winning, general-purpose, non-parametric regressors, which exploit sequential model fitting and gradient descent to minimize a specific loss function. The most popular implementations are tailored to univariate regression and classification tasks, precluding the possibility of capturing multivariate target crosscorrelations and applying structured penalties to the predictions. In this paper, we present a computationally efficient algorithm for fitting multivariate boosted trees. We show that multivariate trees can outperform their univariate counterpart when the predictions are correlated. Furthermore, the algorithm allows to arbitrarily regularize the predictions, so that properties like smoothness, consistency and functional relations can be enforced. We present applications and numerical results related to forecasting and control.
| Item Type: | Scientific journal article, Newspaper article or Magazine article |
|---|---|
| Uncontrolled Keywords: | boosted trees, multivariate regression, forecasting, control, statistical learning |
| Subjects: | Mathematical sciences > Statistics > Statistical modelling Computer sciences > Information systems > Intelligent & expert systems Computer sciences > Artificial intelligence > Machine learning > Automated reasoning |
| Department/unit: | Dipartimento ambiente costruzioni e design > Istituto sostenibilità applicata all'ambiente costruito |
| Depositing User: | Lorenzo Nespoli |
| Date Deposited: | 21 Dec 2022 10:56 |
| Last Modified: | 21 Dec 2022 11:03 |
| URI: | http://repository.supsi.ch/id/eprint/13684 |
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