This function splits the input data into two data.table objects: discrete and continuous. A feature is continuous if
split_columns(data, binary_as_factor = FALSE)
treat binary as categorical? Default is
Features with all missing values will be dropped from the output data, but will be counted towards the column count.
The elements in the output list will have the same class as the input data.
discrete all discrete features
continous all continuous features
num_discrete number of discrete features
num_continuous number of continuous features
num_all_missing number of features with no observations (all values are missing)
output <- split_columns(iris) output$discrete output$continuous output$num_discrete output$num_continuous output$num_all_missing
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