friedman2

Dataset taken from <pkg>mlbench</pkg>. The inputs are 4 independent variables uniformly
distributed over the ranges
$$0 \le x1 \le 100$$
$$40 \pi \le x2 \le 560 \pi$$
$$0 \le x3 \le 1$$
$$1 \le x4 \le 11$$
The outputs are created according to the formula
$$y = (x1^2 + (x2 x3 - (1/(x2 x4)))^2)^{0.5} + e$$
where <em>e</em> is $N(0,sd)$.

datasets

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.

Ronert Obst

parboost

Distributed Model-Based Boosting

friedman2 function

A data frame with 100 rows and 5 variables

format

<url>http://cran.r-project.org/web/packages/mlbench/index.html</url>

friedman2: Benchmark Problem Friedman 2

Description

Arguments

format

source

References