Description Usage Arguments Details Value Examples
ROS
returns a more balanced version of a data set after application
of the Random Over-Sampling algorithm.
1 |
data |
A data frame containing the predictors and the outcome. 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 |
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. |
The Random Over-Sampling algorithm works by appending randomly selected examples from the minority class (with replacement) to the original data set.
A data frame containing a more balanced version of the input data set after application of the Random Over-Sampling algorithm.
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