Construct your own performance measure, used after resampling.
Note that individual training / test set performance values will be set to <code>NA</code>, you
only calculate an aggregated value. If you can define a function that makes sense
for every single training / test set, implement your own <code><a href="/link/Measure?package=mlr&version=2.7" rd-options="" data-mini-rdoc="mlr::Measure">Measure</a></code>.

makeCustomResampledMeasure

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.

makeCustomResampledMeasure: Construct your own resampled performance measure.

Description

Usage

Arguments

Value

See Also