Fuses a learner with preprocessing methods provided by <code><a href="/link/preProcess?package=mlr&version=2.4&to=caret" rd-options="caret" data-mini-rdoc="caret::preProcess">preProcess</a></code>.
 Before training the preprocessing will be performed and the preprocessing model will be stored.
 Before prediction the preprocessing model will transform the test data according to the trained model.<p></p><p> After being wrapped the learner will support missing values although this will only be the case if <code>ppc.knnImpute</code>, <code>ppc.bagImpute</code> or <code>ppc.mediaImpute</code> is set to <code>TRUE</code>.</p>

makePreprocWrapperCaret

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.

makePreprocWrapperCaret: Fuse learner with preprocessing

Description

Usage

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See Also