NAME

       mlpack_preprocess_imputer - impute data

SYNOPSIS

        mlpack_preprocess_imputer -i string -m string -s string [-c double] [-d int] [-V bool] [-o string] [-h -v]

DESCRIPTION

       This  utility  takes  a dataset and converts a 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.csv', and save the result
       to 'result.csv', we could run :

       $ mlpack_preprocess_imputer --input_file dataset --output_file result --missing_value NULL
       --dimension 0 --strategy listwise_deletion

REQUIRED INPUT OPTIONS

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

       --missing_value (-m) [string]
              User defined missing value.

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

OPTIONAL INPUT OPTIONS

       --custom_value (-c) [double] User-defined custom imputation value. Default value 0.

       --dimension (-d) [int]
              The dimension to apply imputation to. Default value 0.

       --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) [string]
              File to save output into. Default value ''.

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.