smote: SMOTE

In nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'

SMOTE

Description

Synthetic Minority Oversampling Technique (SMOTE) algorithm for imbalanced classification data.

Usage

smote(y, x, k = 5, over = NULL, yminor = NULL)

Arguments

y	Vector of response outcome as a factor
x	Matrix of predictors
k	Range of KNN to consider for generation of new data
over	Amount of oversampling of the minority class. If set to NULL then all classes will be oversampled up to the number of samples in the majority class.
yminor	Optional character value specifying the level in y which is to be oversampled. If NULL, this is set automatically to the class with the smallest sample size.

Value

List containing extended matrix x of synthesised data and extended response vector y

References

Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357.

nestedcv documentation built on Oct. 26, 2023, 5:08 p.m.