Creates a learner object, which can be
used like any other learner object.
Internally uses <code><a href="/link/smote?package=mlr&version=2.2" rd-options="" data-mini-rdoc="mlr::smote">smote</a></code> before every model fit.<p></p><p>Note that observation weights do not influence the sampling and are simply passed
down to the next learner.</p>

makeSMOTEWrapper

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.

makeSMOTEWrapper: Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.

Description

Usage

Arguments

Value

See Also