setPredictThreshold

(<a rd-options="" href="/link/Learner?package=mlr&version=2.19.0" data-mini-rdoc="mlr::Learner">Learner</a> | <code>character(1)</code>)
The learner.
If you pass a string the learner will be created via <a rd-options="" href="/link/makeLearner?package=mlr&version=2.19.0" data-mini-rdoc="mlr::makeLearner">makeLearner</a>.

learner

(<a rd-options="" href="/link/numeric?package=mlr&version=2.19.0" data-mini-rdoc="mlr::numeric">numeric</a>)
Threshold to produce class labels. Has to be a named vector, where names correspond to class labels.
Only for binary classification it can be a single numerical threshold for the positive class.
See <a rd-options="" href="/link/setThreshold?package=mlr&version=2.19.0" data-mini-rdoc="mlr::setThreshold">setThreshold</a> for details on how it is applied.
Default is <code>NULL</code> which means 0.5 / an equal threshold for each class.

predict.threshold

See <code>predict.threshold</code> in makeLearner and setThreshold.
For complex wrappers only the top-level <code>predict.type</code> is currently set.

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

setPredictThreshold function

<dl><dt>learner</dt>
<dd>(Learner | <code>character(1)</code>) 
The learner.
If you pass a string the learner will be created via makeLearner.</dd>
<dt>predict.threshold</dt>
<dd>(<a href="/link/numeric?package=mlr&version=2.19.0" data-mini-rdoc="mlr::numeric">numeric</a>) 
Threshold to produce class labels. Has to be a named vector, where names correspond to class labels.
Only for binary classification it can be a single numerical threshold for the positive class.
See setThreshold for details on how it is applied.
Default is <code>NULL</code> which means 0.5 / an equal threshold for each class.</dd></dl>

Arguments

Set the probability threshold the learner should use. — setPredictThreshold

<dl>

<dt>learner</dt>
<dd>(Learner | <code>character(1)</code>) 
The learner.
If you pass a string the learner will be created via makeLearner.</dd>


<dt>predict.threshold</dt>
<dd>(<a href='https://rdrr.io/r/base/numeric.html'>numeric</a>) 
Threshold to produce class labels. Has to be a named vector, where names correspond to class labels.
Only for binary classification it can be a single numerical threshold for the positive class.
See setThreshold for details on how it is applied.
Default is <code>NULL</code> which means 0.5 / an equal threshold for each class.</dd>

</dl>

Set the probability threshold the learner should use.

setPredictThreshold: Set the probability threshold the learner should use.

Description

Usage

Value

Arguments

See Also