makeCostMeasure

(<code>character(1)</code>)
Name of measure.
Default is &#8220;costs&#8221;.

(<code>logical(1)</code>)
Should the measure be minimized?
Otherwise you are effectively specifying a benefits matrix.
Default is <code>TRUE</code>.

minimize

(<a rd-options="" href="/link/matrix?package=mlr&version=2.15.0" data-mini-rdoc="mlr::matrix">matrix</a>)
Matrix of misclassification costs. Rows and columns have to be named with class labels, order does not matter.
Rows indicate true classes, columns predicted classes.

costs

(<code>function</code>)
How to combine costs over all cases for a SINGLE test set?
Note this is not the same as the <code>aggregate</code> argument in <a rd-options="" href="/link/makeMeasure?package=mlr&version=2.15.0" data-mini-rdoc="mlr::makeMeasure">makeMeasure</a>
You can set this as well via <a rd-options="" href="/link/setAggregation?package=mlr&version=2.15.0" data-mini-rdoc="mlr::setAggregation">setAggregation</a>, as for any measure.
Default is <a rd-options="" href="/link/mean?package=mlr&version=2.15.0" data-mini-rdoc="mlr::mean">mean</a>.

combine

(`numeric(1)`)
Best obtainable value for measure.
Default is -`Inf` or `Inf`, depending on `minimize`.

best

(`numeric(1)`)
Worst obtainable value for measure.
Default is `Inf` or -`Inf`, depending on `minimize`.

worst

([character]) 
Name of the measure. Default is `id`.

name

([character]) 
Description and additional notes for the measure. Default is &#8220;&#8221;.

note

Creates a cost measure for non-standard classification error costs.

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

makeCostMeasure function

(<a rd-options='' href='matrix'>matrix</a>)
Matrix of misclassification costs. Rows and columns have to be named with class labels, order does not matter.
Rows indicate true classes, columns predicted classes.

(<code>function</code>)
How to combine costs over all cases for a SINGLE test set?
Note this is not the same as the <code>aggregate</code> argument in <a rd-options='' href='makeMeasure'>makeMeasure</a>
You can set this as well via <a rd-options='' href='setAggregation'>setAggregation</a>, as for any measure.
Default is <a rd-options='' href='mean'>mean</a>.

makeCostMeasure: Creates a measure for non-standard misclassification costs.

Description

Usage

Arguments

Value

See Also