parboost: Distributed Model-Based Boosting
Version 0.1.4

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.

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AuthorRonert Obst <ronert.obst@gmail.com>
Date of publication2015-05-04 01:24:31
MaintainerRonert Obst <ronert.obst@gmail.com>
LicenseGPL-2
Version0.1.4
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("parboost")

Man pages

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.

Functions

coef.parboost Man page Source code
cv_subsample Man page Source code
friedman2 Man page
parboost Man page Source code
parboost-package Man page
parboost_fit Man page Source code
postprocess Man page Source code
predict.parboost Man page Source code
print.parboost Man page Source code
print.summary.parboost Man page Source code
selected.parboost Man page Source code
summary.parboost Man page Source code

Files

inst
inst/CITATION
NAMESPACE
demo
demo/00Index
demo/friedman2.R
NEWS
data
data/friedman2.RData
R
R/parboost-package.R
R/crossvalidation.R
R/methods.R
R/parboost.R
R/postprocess.R
MD5
README
DESCRIPTION
man
man/summary.parboost.Rd
man/selected.parboost.Rd
man/coef.parboost.Rd
man/parboost_fit.Rd
man/parboost.Rd
man/predict.parboost.Rd
man/postprocess.Rd
man/print.summary.parboost.Rd
man/friedman2.Rd
man/print.parboost.Rd
man/cv_subsample.Rd
parboost documentation built on May 19, 2017, 3:54 p.m.