parboost: Distributed Model-Based Boosting

Distributed gradient boosting based on the mboost package. The parboost package is designed to scale up component-wise functional gradient boosting in a distributed memory environment by splitting the observations into disjoint subsets, or alternatively using bootstrap samples (bagging). Each cluster node then fits a boosting model to its subset of the data. These boosting models are combined in an ensemble, either with equal weights, or by fitting a (penalized) regression model on the predictions of the individual models on the complete data.

Getting started

Package details

AuthorRonert Obst <[email protected]>
MaintainerRonert Obst <[email protected]>
Package repositoryView on CRAN
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parboost documentation built on May 2, 2019, 5:08 a.m.