mlpack_nbc(1)

💖 mlpack_nbc — the parametric naive bayes classifier, bestie edition 💖

okay so. this is a manual page for mlpack_nbc(1), a User Command from the mlpack family, and I am so excited to walk you through it because honestly? iconic behavior from a classifier. no cap.

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✨ NAME ✨

mlpack_nbc — the parametric naive bayes classifier. that's it, that's the whole vibe. she classifies. parametrically. naively. beautifully. 🌸

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🎀 SYNOPSIS 🎀

mlpack_nbc [-I bool] [-m unknown] [-l unknown] [-T unknown] [-t unknown] [-V bool] [-M unknown] [-a unknown] [-p unknown] [-h -v]

like. look at all those little flags. a whole wardrobe. 💅

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🦋 DESCRIPTION 🦋

okay so here's the tea: this program trains the Naive Bayes classifier on the labeled training set you give it, or it loads up a model you already made from the model file — and then it may use that trained model to classify points in a test set you hand over. main character energy fr fr.

the training set gets specified with the --training_file (-t) parameter. and where do the labels live, you ask? they can either be the last row of the training set, OR — plot twist — you can pass a separate matrix of labels using the --labels_file (-l) parameter. your call, bestie.

if you're not in the mood to train (valid!), a pre-existing model can be loaded with the --input_model_file (-m) parameter. reusing slay.

then there's --incremental_variance (-I) — that one forces the training to use an incremental algorithm for calculating variance. heads up though: it's slower, but it can help you avoid loss of precision in some cases. so like, tradeoff city. 🙈

if you do want to classify a test set, specify it with the --test_file (-T) parameter, and the classifications get saved with the --predictions_file (-a) predictions parameter. and if saving the trained model is the dream? do it with the --output_model_file (-M) output parameter. save her. she's earned it. 👑

for example — to train a Naive Bayes classifier on the dataset data.csv with labels labels.csv and save the model to nbc_model.bin, the following command may be used:

$ mlpack_nbc --training_file data.csv --labels_file labels.csv --output_model_file nbc_model.bin

then, to use nbc_model.bin to predict the classes of the dataset test_set.csv and save the predicted classes to predictions.csv, the following command may be used:

$ mlpack_nbc --input_model_file nbc_model.bin --test_file test_set.csv --predictions_file predictions.csv

literally the full circle moment. train, save, load, predict. we love a pipeline. 🌈

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💅 OPTIONAL INPUT OPTIONS 💅

the menu of choices. pick your fighters.

• --help (-h) [bool] — default help info. the classic "help me" button, zero judgment. 🫶
• --incremental_variance (-I) [bool] — the variance of each class will be calculated incrementally. slower, but precision-friendly in some cases. dramatic? maybe. useful? sometimes!
• --info [string] — print help on a specific option. default value is ''. so if you want the spotlight on just one flag, this is how.
• --input_model_file (-m) [unknown] — input Naive Bayes model. your previously-trained queen walks back in. 💜
• --labels_file (-l) [unknown] — a file containing labels for the training set. the answers key, basically.
• --test_file (-T) [unknown] — a matrix containing the test set. the exam.
• --training_file (-t) [unknown] — a matrix containing the training set. the study era.
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. she's chatty. she's showing receipts. ⏱️
• --version (-V) [bool] — display the version of mlpack. so you always know which era you're in.

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🍓 OPTIONAL OUTPUT OPTIONS 🍓

where the results go to live their best lives.

• --output_model_file (-M) [unknown] — file to save trained Naive Bayes model to. ✨
• --predictions_file (-a) [unknown] — the matrix in which the predicted labels for the test set will be written.
• --probabilities_file (-p) [unknown] — the matrix in which the predicted probability of labels for the test set will be written. not just what she thinks — how sure she is. confidence, we stan. 🔥

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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. go read, bestie, the lore is real and it's cited. 📚

pro tip from me to you: this page is the entire spec — every flag, every default, every path. don't freestyle the option names. iykyk. 😌

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and that's the whole manual, bestie!! you understood it, you slayed it, periodt. 💜

this was mlpack_nbc(1) from mlpack-4.8.0, dated 22 June 2026. mwah. go classify something iconic. 🎀💖✨