mlpack_preprocess_scale(1)

💅 mlpack_preprocess_scale — the people's manpage

okay so bestie, welcome to mlpack_preprocess_scale(1), from the mlpack crew. this is the tool that takes your data and scales it. glow-up for your feature matrix, no cap. ✨

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🌸 NAME

mlpack_preprocess_scale — scale data. that's it, that's the whole job. she scales. she slays.

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🎀 SYNOPSIS

mlpack_preprocess_scale -i unknown [-r double] [-m unknown] [-f bool] [-e int] [-b int] [-a string] [-s int] [-V bool] [-o unknown] [-M unknown] [-h -v]

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🦋 DESCRIPTION

so this utility takes a dataset and does feature scaling on it using one of six scaler methods, namely:

• max_abs_scaler
• mean_normalization
• min_max_scaler
• standard_scaler
• pca_whitening
• zca_whitening

she takes a matrix in as --input_file (-i) and a scaling method type that you specify with --scaler_method (-a) — and if you don't say anything, the default is standard scaler. then she outputs a matrix with the scaled feature. iconic behavior honestly. 💖

the output scaled feature matrix can be saved with the --output_file (-o) output parameters.

the model used to scale the features can be saved with --output_model_file (-M) and can later be loaded back using --input_model_file (-m). save your work bestie, always. 🫶

so, a simple example where we want to scale the dataset X.csv into X_scaled.csv with standard_scaler as scaler_method — we could run:

$ mlpack_preprocess_scale --input_file X.csv --output_file X_scaled.csv --scaler_method standard_scaler

a simple example where we want to whiten the dataset X.csv into X_whitened.csv with PCA as whitening_method and use 0.01 as the regularization parameter — we could run:

$ mlpack_preprocess_scale --input_file X.csv --output_file X_scaled.csv --scaler_method pca_whitening --epsilon 0.01

you can also retransform the scaled dataset back using --inverse_scaling (-f). an example to rescale X_scaled.csv into X.csv using the saved model --input_model_file (-m) is:

$ mlpack_preprocess_scale --input_file X_scaled.csv --output_file X.csv --inverse_scaling --input_model_file saved.bin

another simple example where we want to scale the dataset X.csv into X_scaled.csv with min_max_scaler as the scaler method, where the scaling range is 1 to 3 instead of the default 0 to 1 — we could run:

$ mlpack_preprocess_scale --input_file X.csv --output_file X_scaled.csv --scaler_method min_max_scaler --min_value 1 --max_value 3

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🔥 REQUIRED INPUT OPTIONS

you literally cannot skip these, bestie. she needs them. 🙈

• --input_file (-i) [unknown] — Matrix containing data. that's your whole vibe right there.

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✨ OPTIONAL INPUT OPTIONS

the fun zone. mix and match, go wild. 💜

• --epsilon (-r) [double] — regularization Parameter for pcawhitening, or zcawhitening. should be between -1 to 1. Default value 1e-06.
• --help (-h) [bool] — Default help info. she's got your back when you're lost.
• --info [string] — Print help on a specific option. Default value ''.
• --input_model_file (-m) [unknown] — Input Scaling model.
• --inverse_scaling (-f) [bool] — Inverse Scaling to get original dataset.
• --max_value (-e) [int] — Ending value of range for min_max_scaler. Default value 1.
• --min_value (-b) [int] — Starting value of range for min_max_scaler. Default value 0.
• --scaler_method (-a) [string] — method to use for scaling, the default is standard_scaler. Default value 'standard_scaler'.
• --seed (-s) [int] — Random seed (0 for std::time(NULL)). Default value 0.
• --verbose (-v) [bool] — Display informational messages and the full list of parameters and timers at the end of execution. the receipts, basically. 🧾
• --version (-V) [bool] — Display the version of mlpack.

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💖 OPTIONAL OUTPUT OPTIONS

where the results live. show them off. 👑

• --output_file (-o) [unknown] — Matrix to save scaled data to.
• --output_model_file (-M) [unknown] — Output scaling model.

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🌈 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. the lore is out there, go read it. 📚

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okay that's the whole page bestie, you ate. this was mlpack_preprocess_scale(1), part of mlpack-4.8.0, dated 22 June 2026. go forth and scale responsibly. periodt. 💅🌸✨🦋