Description Usage Arguments Details Value Examples
ROS
returns a more balanced version of a data set after application
of the Random OverSampling 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 OverSampling 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 OverSampling algorithm.
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