SMARTboost Learning for Tabular Data
提出SMARTboost算法，通过平滑对称加法回归树提升梯度提升机在函数平滑或小样本、噪声数据下的预测精度，在计量经济学常见模型和全球股票收益等应用中优于XGBoost和BART。
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.