parboost_fit

A numeric vector containing the indices
of the subsample

subsample_indices

A data frame containing the variables in the
model. It is recommended to use path_to_data instead for
IO efficiency. Defaults to NULL

data

path_to_data

What function should be used to import
the data?

data_import_function

Optional preprocessing function to apply to
the data passed from parboost

preprocessing

seed

formula

Character string determining the type of base learner.

baselearner

family

control

tree_controls

cores_cv

folds

stepsize_mstop


Internal function to fit mboost model on a subset of the data


internal

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.

parboost_fit: Fit individual parboost component using mboost

Description

Usage

Arguments

Value

Details