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mlpack_preprocess_imputer - impute data

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_imputer(1)

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

Name

mlpack_preprocess_imputer - impute data

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 ''.

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'.

Synopsis

mlpack_preprocess_imputer-istring-mstring-sstring [-cdouble] [-dint] [-Vbool] [-ostring] [-h-v]

See Also