KTBoost: Combined Kernel and Tree Boosting

We introduce a novel boosting algorithm called 'KTBoost' which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree or reproducing kernel Hilbert space (RKHS) regression function to the ensemble of base learners. Intuitively, the idea is that discontinuous trees and continuous RKHS regression functions complement each other, and that this combination allows for better learning of functions that have parts with varying degrees of regularity such as discontinuities and smooth parts. We empirically show that KTBoost significantly outperforms both tree and kernel boosting in terms of predictive accuracy in a comparison on a wide array of data sets.

Automated summary

The KTBoost algorithm combines kernel boosting and tree boosting, adding either a regression tree or an RKHS regression function in each boosting iteration. The combination of discontinuous trees and continuous RKHS regression functions allows for better learning of functions with parts of varying degrees of regularity. Empirical results demonstrate that KTBoost significantly outperforms both tree and kernel boosting in terms of predictive accuracy.