In each iteration, samples one minority class element x1, then one of x1's nearest neighbors: x2.
Both points are now interpolated / convex-combined, resulting in a new virtual data point x3
for the minority class.<p></p><p>The method handles factor features, too. The gower distance is used for nearest neighbor
calculation, see <code><a href="/link/daisy?package=mlr&version=2.5&to=cluster" rd-options="cluster" data-mini-rdoc="cluster::daisy">daisy</a></code>.
For interpolation, the new factor level for x3
is sampled from the two given levels of x1 and x2 per feature.</p>

smote

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.

smote: Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.

Description

Usage

Arguments

Value

References

See Also