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.
|Author||Ronert Obst <email@example.com>|
|Date of publication||2015-05-04 01:24:31|
|Maintainer||Ronert Obst <firstname.lastname@example.org>|
coef.parboost: Print coefficients for base learners with a notion of...
cv_subsample: Cross-validation for mboost models
friedman2: Benchmark Problem Friedman 2
parboost: Distributed gradient boosting based on the 'mboost' package.
parboost_fit: Fit individual parboost component using mboost
postprocess: Postprocess parboost ensemble components
predict.parboost: Generate predictions from parboost object
print.parboost: Prints a short description of a parboost object.
print.summary.parboost: Prints a summary of a parboost object.
selected.parboost: Selected base learners
summary.parboost: Prints a summary of a parboost object.