getMultilabelBinaryPerformances

[<code><a rd-options="" href="/link/Prediction?package=mlr&version=2.10" data-mini-rdoc="mlr::Prediction">Prediction</a></code>]
Multilabel Prediction object.

pred

[<code><a rd-options="" href="/link/Measure?package=mlr&version=2.10" data-mini-rdoc="mlr::Measure">Measure</a></code> | list of <code><a rd-options="" href="/link/Measure?package=mlr&version=2.10" data-mini-rdoc="mlr::Measure">Measure</a></code>]
Performance measure(s) to evaluate, must be applicable to binary classification performance.
Default is <code>mmce</code>.

measures

Measures the quality of each binary label prediction w.r.t. some binary classification
performance measure.

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

getMultilabelBinaryPerformances function

[<code><a rd-options='' href='Prediction'>Prediction</a></code>]
Multilabel Prediction object.

[<code><a rd-options='' href='Measure'>Measure</a></code> | list of <code><a rd-options='' href='Measure'>Measure</a></code>]
Performance measure(s) to evaluate, must be applicable to binary classification performance.
Default is <code>mmce</code>.

getMultilabelBinaryPerformances: Retrieve binary classification measures for multilabel classification predictions.

Description

Usage

Arguments

Value

See Also

Examples