estimateResidualVariance

(<a rd-options="" href="/link/Learner?package=mlr&version=2.19.0" data-mini-rdoc="mlr::Learner">Learner</a> or <a rd-options="" href="/link/WrappedModel?package=mlr&version=2.19.0" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a>)
Learner or wrapped model.

(<a rd-options="" href="/link/RegrTask?package=mlr&version=2.19.0" data-mini-rdoc="mlr::RegrTask">RegrTask</a>)
Regression task.
If missing, <code>data</code> and <code>target</code> must be supplied.

task

(<a rd-options="" href="/link/data.frame?package=mlr&version=2.19.0" data-mini-rdoc="mlr::data.frame">data.frame</a>)
A data frame containing the features and target variable.
If missing, <code>task</code> must be supplied.

data

(<code>character(1)</code>)
Name of the target variable.
If missing, <code>task</code> must be supplied.

target

Estimate the residual variance of a regression model on a given task.
If a regression learner is provided instead of a model, the model is
trained (see train) first.

Interface to a large number of classification and
regression techniques, including machine-readable parameter
descriptions. There is also an experimental extension for survival
analysis, clustering and general, example-specific cost-sensitive
learning. Generic resampling, including cross-validation,
bootstrapping and subsampling.  Hyperparameter tuning with modern
optimization techniques, for single- and multi-objective problems.
Filter and wrapper methods for feature selection. Extension of basic
learners with additional operations common in machine learning, also
allowing for easy nested resampling.  Most operations can be
parallelized.

Patrick Schratz

Machine Learning in R

Bernd Bischl

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

Annette Spooner

estimateResidualVariance function

<dl><dt>x</dt>
<dd>(Learner or WrappedModel) 
Learner or wrapped model.</dd>
<dt>task</dt>
<dd>(RegrTask) 
Regression task.
If missing, <code>data</code> and <code>target</code> must be supplied.</dd>
<dt>data</dt>
<dd>(<a href="/link/data.frame?package=mlr&version=2.19.0" data-mini-rdoc="mlr::data.frame">data.frame</a>) 
A data frame containing the features and target variable.
If missing, <code>task</code> must be supplied.</dd>
<dt>target</dt>
<dd>(<code>character(1)</code>) 
Name of the target variable.
If missing, <code>task</code> must be supplied.</dd></dl>

Arguments

Estimate the residual variance. — estimateResidualVariance

<dl>

<dt>x</dt>
<dd>(Learner or WrappedModel) 
Learner or wrapped model.</dd>


<dt>task</dt>
<dd>(RegrTask) 
Regression task.
If missing, <code>data</code> and <code>target</code> must be supplied.</dd>


<dt>data</dt>
<dd>(<a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a>) 
A data frame containing the features and target variable.
If missing, <code>task</code> must be supplied.</dd>


<dt>target</dt>
<dd>(<code>character(1)</code>) 
Name of the target variable.
If missing, <code>task</code> must be supplied.</dd>

</dl>

Estimate the residual variance.

estimateResidualVariance: Estimate the residual variance.

Description

Usage

Arguments