A performance measure is evaluated after a single train/predict step and returns a single number to assess the quality
of the prediction (or maybe only the model, think AIC).
The measure itself knows whether it wants to be minimized or maximized and for what tasks it is applicable.All supported measures can be found by <code><a href="/link/listMeasures?package=mlr&version=2.2" rd-options="" data-mini-rdoc="mlr::listMeasures">listMeasures</a></code> or as a table
in the tutorial appendix: <url>http://berndbischl.github.io/mlr/tutorial/html/measures/</url>.If you want a measure for a misclassification cost matrix, look at <code><a href="/link/makeCostMeasure?package=mlr&version=2.2" rd-options="" data-mini-rdoc="mlr::makeCostMeasure">makeCostMeasure</a></code>.
If you want to implement your own measure, look at <code><a href="/link/makeMeasure?package=mlr&version=2.2" rd-options="" data-mini-rdoc="mlr::makeMeasure">makeMeasure</a></code>.

datasets

measures

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.

measures: Performance measures.

Description

Usage

Arguments

docType

format

See Also