setPredictThreshold

[<code><a rd-options="" href="/link/Learner?package=mlr&version=2.10" data-mini-rdoc="mlr::Learner">Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options="" href="/link/makeLearner?package=mlr&version=2.10" data-mini-rdoc="mlr::makeLearner">makeLearner</a></code>.

learner

[<code>numeric</code>]
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 <code><a rd-options="" href="/link/setThreshold?package=mlr&version=2.10" data-mini-rdoc="mlr::setThreshold">setThreshold</a></code> 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 <code><a rd-options="" href="/link/makeLearner?package=mlr&version=2.10" data-mini-rdoc="mlr::makeLearner">makeLearner</a></code> and <code><a rd-options="" href="/link/setThreshold?package=mlr&version=2.10" data-mini-rdoc="mlr::setThreshold">setThreshold</a></code>. 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.

Bernd Bischl

Machine Learning in R

setPredictThreshold function

[<code><a rd-options='' href='Learner'>Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options='' href='makeLearner'>makeLearner</a></code>.

[<code>numeric</code>]
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 <code><a rd-options='' href='setThreshold'>setThreshold</a></code> for details on how it is applied.
Default is <code>NULL</code> which means 0.5 / an equal threshold for each class.

setPredictThreshold: Set the probability threshold the learner should use.

Description

Usage

Arguments

Value

See Also