NAME

       mlpack_preprocess_imputer - impute data

SYNOPSIS

        mlpack_preprocess_imputer [-h] [-v]

DESCRIPTION

       This  utility  takes  a  dataset  and converts user defined missing variable to another to
       provide more meaningful analysis

       The program does not modify the original file, but instead makes a separate file  to  save
       the  output  data;  You can save the output by specifying the file name with --output_file
       (-o).

       For example, if we consider 'NULL' in dimension 0 to be a missing  variable  and  want  to
       delete  whole  row  containing the NULL in the column-wise dataset, and save the result to
       result.csv, we could run

       $  mlpack_preprocess_imputer  -i  dataset.csv  -o  result.csv  -m   NULL   -d   0   >   -s
       listwise_deletion

REQUIRED INPUT OPTIONS

       --input_file (-i) [string]
              File containing data,

OPTIONAL INPUT OPTIONS

       --custom_value (-c) [double] user_defined custom value Default value 0.

       --dimension (-d) [int]
              the dimension to apply imputation Default value

              0.

       --help (-h)
              Default help info.

       --info [string]
              Get  help  on a specific module or option.  Default value ''.  --missing_value (-m)
              [string] User defined missing value Default value ''.

       --strategy (-s) [string]
              imputation strategy to be applied. Strategies should be one  of  'custom',  'mean',
              'median', and ’listwise_deletion'. Default value ''.

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

       --version (-V)
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_file (-o) [string]
              File to save output Default value ''.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

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

                                                      mlpack_preprocess_imputer(16 November 2017)