R: Binarize Data

preprocess_binarize {mlpack} | R Documentation

## Binarize Data

### Description

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.

### Usage

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

### Arguments

`input` | Input data matrix (numeric matrix).
`dimension` | Dimension to apply the binarization. If not set, the program will binarize every dimension by default. Default value "0" (integer).
`threshold` | Threshold to be applied for binarization. If not set, the threshold defaults to 0.0. Default value "0" (numeric).
`verbose` | Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical).

### Details

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.

### Value

A list with several components:

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

### Author(s)

mlpack developers

### Examples

    # 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)

[Package mlpack version 3.4.2.1 Index]