## Over-sampling

Over-sampling is the act of synthetically generating observations for the minority classes. This is done either by random sampling or using more advanced methods.

step_upsample()
Up-Sample a Data Set Based on a Factor Variable
step_smote()
Apply SMOTE Algorithm
step_smotenc()
Apply SMOTENC algorithm
step_bsmote()
Apply borderline-SMOTE Algorithm
step_adasyn()
step_rose()
Apply ROSE Algorithm

## Under-sampling

Under-sampling is the act of removing observations from the majority classes.

step_downsample()
Down-Sample a Data Set Based on a Factor Variable
step_nearmiss()
Remove Points Near Other Classes
step_tomek()

## Methods

Some of the methods implemented in this package as steps are also available as their own functions.

smote()
SMOTE Algorithm
smotenc()
SMOTENC Algorithm
bsmote()
borderline-SMOTE Algorithm
adasyn()
nearmiss()
Remove Points Near Other Classes
tomek()
circle_example
tidy(<step_adasyn>) tidy(<step_bsmote>) tidy(<step_downsample>) tidy(<step_nearmiss>) tidy(<step_rose>) tidy(<step_smote>) tidy(<step_smotenc>) tidy(<step_tomek>) tidy(<step_upsample>)