Smartboost Learning for Tabular Data

34 Pages Posted: 6 Dec 2021 Last revised: 9 May 2024

Date Written: December 1, 2021

Abstract

We introduce SMARTboost (boosting of symmetric smooth additive regression trees), an extension of gradient boosting machines with improved accuracy when the underlying function is smooth or the sample small or noisy. In extensive simulations, we find that the combination of smooth symmetric trees and of carefully designed priors gives SMARTboost a large edge (in comparison with XGBoost and BART) on data generated by the most common parametric models in econometrics, and on a variety of other smooth functions. XGBoost outperforms SMARTboost only when the sample is large and the underlying function is highly discontinuous. SMARTboost's performance is illustrated in two applications to global equity returns and realized volatility prediction.

Keywords: nonlinear regression, boosting, smooth symmetric trees, oblivious trees, Bayesian priors, cross-validation, marginal effects

JEL Classification: C51, C52, C53, C55, C56, C58, G17

Suggested Citation

Giordani, Paolo, Smartboost Learning for Tabular Data (December 1, 2021). Available at SSRN: https://ssrn.com/abstract=3975543 or http://dx.doi.org/10.2139/ssrn.3975543

Paolo Giordani (Contact Author)

Norwegian Business School ( email )

Nydalsveien 37
Oslo, 0442
Norway

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