focal (1) mlpack_preprocess_imputer.1.gz

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, 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.

       --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.