oversample: Over- or undersample binary classification task to handle...
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also

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.

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oversample(task, rate, cl = NULL)

undersample(task, rate, cl = NULL)

`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.