NAME

       mlpack_preprocess_one_hot_encoding - one hot encoding

SYNOPSIS

        mlpack_preprocess_one_hot_encoding -i string [-d vector] [-V bool] [-o unknown] [-h -v]

DESCRIPTION

       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 (-d)', then all categorical-type dimensions will be
       one-hot encoded. Otherwise, only the dimensions given in '--dimensions (-d)' will be one-hot encoded.

       The output matrix with encoded features may be saved with the '--output_file (-o)' parameters.

       So, a simple example where we want to encode 1st and 3rd feature from dataset ’X.csv' into 'X_output.csv'
       would be

       $  mlpack_preprocess_one_hot_encoding  --input_file  X.arff  --output_file  X_ouput.csv  --dimensions   1
       --dimensions 3

REQUIRED INPUT OPTIONS

       --input_file (-i) [string]
              Matrix containing data.

OPTIONAL INPUT OPTIONS

       --dimensions (-d) [vector]
              Index  of  dimensions  that need to be one-hot encoded (if unspecified, all categorical dimensions
              are one-hot encoded). Default value [].

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_file (-o) [unknown] Matrix to save one-hot encoded features data to.

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  consult  the  documentation
       found at http://www.mlpack.org or included with your distribution of mlpack.

mlpack-4.5.1                                     29 January 2025           mlpack_preprocess_one_hot_encoding(1)