mlpack_preprocess_imputer(1)

💖 mlpack_preprocess_imputer — impute data 💖

okay bestie, welcome to the glow-up station for your dataset 🧚✨ this page is all about mlpack_preprocess_imputer, a little utility that patches up the holes in your data so your analysis can actually serve looks.

✨ SYNOPSIS ✨

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

🌸 DESCRIPTION 🌸

so the whole vibe here is: this utility takes a dataset and converts a user-defined missing variable into another one, so that your analysis is actually meaningful instead of just a mess of gaps 💀

and like, super important — the program does not modify the original matrix. it's not out here disrespecting your input file. instead it makes a whole separate matrix for the output, and that happens via the --output_file (-o) option 🫶

for example, if we're considering NaN values in dimension 0 to be the missing variable, and we want to delete a whole data point whenever it contains a NaN in the column-wise 'dataset.csv', we could run:

$ mlpack_preprocess_imputer --input_file dataset.csv --output_file
result.csv --dimension 0 --strategy listwise_deletion

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💅 REQUIRED INPUT OPTIONS 💅

no skips allowed here, these are mandatory periodt:

• --strategy (-s) [string] — the imputation strategy that's gonna be applied. Strategies should be one of 'custom', 'mean', 'median', and 'listwise_deletion'.

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🎀 OPTIONAL INPUT OPTIONS 🎀

the mix-and-match section, pick your faves:

• --custom_value (-c) [double] — user-defined custom imputation value; only used if the strategy is 'custom'. default value 0. (iykyk, only for the custom girlies 💅)
• --dimension (-d) [int] — the dimension to apply imputation to. if you don't specify it, missing values will be imputed in every dimension. default value 0.
• --help (-h) [bool] — default help info. the classic cry for help 🥺
• --info [string] — print help on a specific option. default value ''.
• --input_file (-i) [unknown] — the input matrix to impute values for. your raw material bestie 🌈
• --missing_value (-m) [double] — the value used to indicate missing elements that will be imputed. default value nan. (yes, literally nan, iconic)
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. main character energy, very chatty ✨
• --version (-V) [bool] — display the version of mlpack.

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⭐ OPTIONAL OUTPUT OPTIONS ⭐

• --output_file (-o) [unknown] — the matrix to output, which will have imputed values. this is the glow-up reveal 💖

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👑 ADDITIONAL INFORMATION 👑

for further information — and we mean the relevant papers, citations, and theory — consult the documentation found at http://www.mlpack.org or the docs included with your distribution of mlpack. go read, scholar 🦋

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and that's the whole page bestie, no cap, every last detail accounted for 😭💜

this page came from mlpack-4.8.0, dated 22 June 2026, for mlpack_preprocess_imputer(1) — mwah, slay, go impute something gorgeous ✨💅