R: Over- or undersample binary classification task to handle...

oversample {mlr} | R Documentation

## Over- or undersample binary classification task to handle class imbalancy.

### Description

Oversampling: For a given class (usually the smaller one) all existing observations are taken and copied and extra observations are added by randomly sampling with replacement from this class.

Undersampling: For a given class (usually the larger one) the number of observations is reduced (downsampled) by randomly sampling without replacement from this class.

### Usage

    oversample(task, rate, cl = NULL)

    undersample(task, rate, cl = NULL)

### Arguments

`task` | (Task) The task.
`rate` | (`numeric(1)`) Factor to upsample or downsample a class. For undersampling: Must be between 0 and 1, where 1 means no downsampling, 0.5 implies reduction to 50 percent and 0 would imply reduction to 0 observations. For oversampling: Must be between 1 and `Inf`, where 1 means no oversampling and 2 would mean doubling the class size.
`cl` | (`character(1)`) Which class should be over- or undersampled. If `NULL`, `oversample` will select the smaller and `undersample` the larger class.

### Value

Task.

### See Also

Other imbalancy: `makeOverBaggingWrapper()`, `makeUndersampleWrapper()`, `smote()`

[Package mlr version 2.17.1 Index]