preprocess_binarize: Binarize Data

View source: R/preprocess_binarize.R

preprocess_binarizeR Documentation

Binarize Data


A utility to binarize a dataset. Given a dataset, this utility converts each value in the desired dimension(s) to 0 or 1; this can be a useful preprocessing step.


preprocess_binarize(input, dimension = NA, threshold = NA, verbose = FALSE)



Input data matrix (numeric matrix).


Dimension to apply the binarization. If not set, the program will binarize every dimension by default. Default value "0" (integer).


Threshold to be applied for binarization. If not set, the threshold defaults to 0.0. Default value "0" (numeric).


Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical).


This utility takes a dataset and binarizes the variables into either 0 or 1 given threshold. User can apply binarization on a dimension or the whole dataset. The dimension to apply binarization to can be specified using the "dimension" parameter; if left unspecified, every dimension will be binarized. The threshold for binarization can also be specified with the "threshold" parameter; the default threshold is 0.0.

The binarized matrix may be saved with the "output" output parameter.


A list with several components:


Matrix in which to save the output (numeric matrix).


mlpack developers


# For example, if we want to set all variables greater than 5 in the dataset
# "X" to 1 and variables less than or equal to 5.0 to 0, and save the result
# to "Y", we could run

## Not run: 
output <- preprocess_binarize(input=X, threshold=5)
Y <- output$output

## End(Not run)

# But if we want to apply this to only the first (0th) dimension of "X",  we
# could instead run

## Not run: 
output <- preprocess_binarize(input=X, threshold=5, dimension=0)
Y <- output$output

## End(Not run)

mlpack documentation built on Oct. 29, 2022, 1:06 a.m.