mlpack (version 4.5.1)

preprocess_one_hot_encoding: One Hot Encoding

Description

A utility to do one-hot encoding on features of dataset.

Usage

preprocess_one_hot_encoding(
  input,
  dimensions = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Value

A list with several components:

output

Matrix to save one-hot encoded features data to (numeric matrix).

Arguments

input

Matrix containing data (numeric matrix/data.frame with info).

dimensions

Index of dimensions that need to be one-hot encoded (if unspecified, all categorical dimensions are one-hot encoded) (integer vector).

verbose

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

Author

mlpack developers

Details

This utility takes a dataset and a vector of indices and does one-hot encoding of the respective features at those indices. Indices represent the IDs of the dimensions to be one-hot encoded.

If no dimensions are specified with "dimensions", then all categorical-type dimensions will be one-hot encoded. Otherwise, only the dimensions given in "dimensions" will be one-hot encoded.

The output matrix with encoded features may be saved with the "output" parameters.

Examples

# So, a simple example where we want to encode 1st and 3rd feature from
# dataset "X" into "X_output" would be

if (FALSE) {
output <- preprocess_one_hot_encoding(input=X, dimensions=1, dimensions=3)
X_ouput <- output$output
}