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)