mlpack_bayes...ar_regression(1)

✨ mlpack_bayesian_linear_regression ✨

okay so bestie, this is the manual page for mlpack_bayesian_linear_regression — it's part of the mlpack user commands, and it's the one that does the Bayesian linear regression thing. yes it's a long name. no you're not gonna forget it. let's go 💅

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🌸 SYNOPSIS 🌸

mlpack_bayesian_linear_regression [-c bool] [-i unknown] [-m unknown] [-r unknown] [-s bool] [-t unknown] [-V bool] [-M unknown] [-o unknown] [-u unknown] [-h -v]

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🦋 DESCRIPTION 🦋

so this is an implementation of Bayesian linear regression, like fully. It's a probabilistic view and implementation of linear regression — meaning it's not just "draw a line through the dots," it's "what does the distribution of possible lines look like," and yeah, that's the whole vibe. 🌈

The final solution comes out of computing a posterior distribution from a gaussian likelihood and a zero mean gaussian isotropic prior distribution on the solution. Optimization here is AUTOMATIC and does not require cross validation (iconic, we love not having to hand-tune things). The optimization is done by maximization of the evidence function, and the parameters get tuned during the maximization of the marginal likelihood. This whole procedure includes the Ockham's razor that penalizes over complex solutions — so it's giving "keep it simple bestie" but mathematically 🎀

This program can:

• train a Bayesian linear regression model, OR
• load a model from file, OR
• output regression predictions for a test set, AND
• save the trained model to a file.

To train a BayesianLinearRegression model, the --input_file (-i) and --responses_file (-r) parameters must be given. The --center (-c) and --scale (-s) parameters control the centering and the normalizing options (so true/false on whether you want the data centered and normalized, respectively). A trained model can be saved with --output_model_file (-M). And if you don't want to train anything at all, you can just pass a model in via the --input_model_file (-m) parameter. no training, just vibes ✨

The program can also provide predictions for test data, using either the trained model or the given input model. Test points get specified with the --test_file (-t) parameter. Predicted responses to the test points can be saved with the --predictions_file (-o) output parameter. And if you want the uncertainty too (which, honestly, why wouldn't you), the corresponding standard deviation can be saved by specifying the --stds_file (-u) parameter. 🫶

so like, for example, this command trains a model on the data data.csv and responses responses.csv with center set to true and scale set to false (so, Bayesian linear regression is being solved, and then the model is saved to blr_model.bin):

$ mlpack_bayesian_linear_regression --input_file data.csv --responses_file responses.csv --center --scale --output_model_file blr_model.bin

and then the following command uses that blr_model.bin to provide predicted responses for the data test.csv, saving those responses to test_predictions.csv:

$ mlpack_bayesian_linear_regression --input_model_file blr_model.bin --test_file test.csv --predictions_file test_predictions.csv

and because the estimator computes a predictive distribution instead of a plain little point estimate (main character behavior, frankly), the --stds_file (-u) parameter lets you save the prediction uncertainties too:

$ mlpack_bayesian_linear_regression --input_model_file blr_model.bin --test_file test.csv --predictions_file test_predictions.csv --stds_file stds.csv

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👑 OPTIONAL INPUT OPTIONS 👑

• --center (-c) [bool] — if you turn this on, it centers the data and fits the intercept. yes queen, intercept included 💖
• --help (-h) [bool] — the default help info. classic.
• --info [string] — print help on one specific option. Default value is ''.
• --input_file (-i) [unknown] — the matrix of covariates (X). that's your input data bestie.
• --input_model_file (-m) [unknown] — a trained BayesianLinearRegression model to use. (skip the training, just bring your own.)
• --responses_file (-r) [unknown] — the matrix of responses/observations (y). the thing you're trying to predict.
• --scale (-s) [bool] — if enabled, scales each feature by their standard deviations. normalize the girlies 💅
• --test_file (-t) [unknown] — matrix containing the points to regress on (test points).
• --verbose (-v) [bool] — displays informational messages plus the full list of parameters and timers at the end of execution. for the nosy ones (me. it's me. 🙈)
• --version (-V) [bool] — displays the version of mlpack.

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🍓 OPTIONAL OUTPUT OPTIONS 🍓

• --output_model_file (-M) [unknown] — output BayesianLinearRegression model. save your work fr fr.
• --predictions_file (-o) [unknown] — if --test_file is specified, this file is where the predicted responses will be saved.
• --stds_file (-u) [unknown] — if specified, this is where the standard deviations of the predictive distribution will be saved.

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🌟 ADDITIONAL INFORMATION 🌟

for further info — like relevant papers, citations, and all that theory — go consult the documentation at http://www.mlpack.org, or the docs that came included with your distribution of mlpack. iykyk 📚

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ngl bestie, --stds_file is the sleeper hit here. predictions with uncertainty? that's range. that's depth. that's a model that knows what it doesn't know. 🧚

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and that's the page!! 💜 this was the manual for mlpack_bayesian_linear_regression from mlpack-4.8.0, dated 22 June 2026. slay, go train something, and remember: automatic optimization, no cross validation, Ockham's razor said keep it cute and simple. periodt ✨🎀🦋