Man page - mlpack_preprocess_imputer(1)
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Manual
mlpack_preprocess_imputer
NAMESYNOPSIS
DESCRIPTION
REQUIRED INPUT OPTIONS
OPTIONAL INPUT OPTIONS
OPTIONAL OUTPUT OPTIONS
ADDITIONAL INFORMATION
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 withā --output_file ( -o )ā.
For example, if we consider āNULLā in dimension 0 to be a missing variable and want to delete whole row containing the NULL in the column-wiseādataset.csvā, and save the result to āresult.csvā, 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 ācustomā, āmeanā, āmedianā, and ālistwise_deletionā.
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 āā.
--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 āā.
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.