Publication Type:
Journal Article
Citation:
Statistics and Computing, 2016, 26 (1-2), pp. 93 - 105
Issue Date:
2016-01-01
© 2014, Springer Science+Business Media New York. A new data science tool named wavelet-based gradient boosting is proposed and tested. The approach is special case of componentwise linear least squares gradient boosting, and involves wavelet functions of the original predictors. Wavelet-based gradient boosting takes advantages of the approximate $$\ell _1$$ℓ1 penalization induced by gradient boosting to give appropriate penalized additive fits. The method is readily implemented in R and produces parsimonious and interpretable regression fits and classifiers.