Description Usage Arguments Details Value References

`SMOTE`

returns a more balanced version of a data set after
application of the SMOTE algorithm.

1 2 |

`data` |
A data frame containing the predictors and the outcome. The
predictors must be numeric and the outcome must be both a binary valued
factor and the last column of |

`perc_min` |
The desired % size of the minority class relative to the
whole data set. For instance, if |

`perc_over` |
% of examples to append to the input data set relative
to the size of the minority class. For instance, if |

`k` |
Number of nearest neighbours to compute for each example in the minority class. |

`over_replace` |
A logical value indicating whether the neighbours
picked from the |

`classes` |
A named vector identifying the majority and the minority classes. The names must be "Majority" and "Minority". This argument is only useful if the function is called inside another sampling function. |

SMOTE is an over-sampling algorithm that synthesises new examples in the line segment joining two close minority class examples.

A data frame containing a more balanced version of the input data set after application of the SMOTE algorithm.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P.
(2002). SMOTE: synthetic minority over-sampling technique. *Journal
of artificial intelligence research*, *16*, 321-357.

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