multisample

multisample.cross_fold

multisample.random

data

folds

The dependent variable in the data. Used only if <code>preserve_distribution=TRUE</code>

dependent

Logical, only applicable if the dependent variable is a factor

preserve_distribution

The fraction of data to be used as holdout set

holdout

iterations

Make multiple samples of data<code>cross_fold</code>: Make 'folds' samples of the data, so <code>all(rbind(folds)==row.names(data))=TRUE</code><code>random</code>: Makes <code>iterations</code> random samples of size <code>holdout * nrow(data)</code>

Provides wrapper functions for running classification and
regression tests using different machine learning techniques, such as Random
Forests and decision trees. The package provides standardized methods for
preparing data to suit the algorithm's needs, training a model, making
predictions, and evaluating results. Also, some functions are provided to run
multiple instances of a test.

multisample: Make multiple samples of data

Description

Usage

Arguments

Value