Creates a learner object, which can be
used like any other learner object.
Internally uses <code><a href="/link/smote?package=mlr&version=2.0" 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 a n experimental extension for surival analysis and cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and
    subsampling. Hyperparameter tuning with modern optimization techniques.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning.

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

Description

Usage

Arguments

Value