mlpack_lars(1)

✨ NAME ✨

okay bestie, this is mlpack_lars(1) — the manual page for the mlpack_lars command, and it is literally the LARS girl of the mlpack lineup 💅

---

🌸 SYNOPSIS 🌸

mlpack_lars [-i unknown] [-m unknown] [-l double] [-L double] [-n bool] [-N bool] [-r unknown] [-t unknown] [-c bool] [-V bool] [-M unknown] [-o unknown] [-h -v]

---

💖 DESCRIPTION 💖

so what is this? It's an implementation of LARS: Least Angle Regression (Stagewise/laSso). Think of it as a stage-wise homotopy-based algorithm that handles both L1-regularized linear regression (aka LASSO) and L1+L2-regularized linear regression (aka Elastic Net). highkey iconic.

This program can train a LARS/LASSO/Elastic Net model, or load a model from a file, or output regression predictions for a test set, or save the trained model to a file. multi-talented fr fr. The LARS algorithm breakdown goes like this:

Let X be a matrix where each row is a point and each column is a dimension, and let y be a vector of targets.

The Elastic Net problem is to solve

min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 +
  0.5 lambda_2 ||beta||_2^2

now here's the vibe check on which problem you're actually solving 👇

• If lambda1 > 0 and lambda2 = 0, the problem is the LASSO.
• If lambda1 > 0 and lambda2 > 0, the problem is the Elastic Net.
• If lambda1 = 0 and lambda2 > 0, the problem is ridge regression.
• If lambda1 = 0 and lambda2 = 0, the problem is unregularized linear regression.

⚠️ okay important, for efficiency reasons it is not recommended to use this algorithm with --lambda1 (-l) = 0. In that case, use the linear_regression program, which implements both unregularized linear regression and ridge regression. not me trying to use the wrong tool 😭

---

To train a LARS/LASSO/Elastic Net model, the --input_file (-i) and --responses_file (-r) parameters must be given. The --lambda1 (-l), --lambda2 (-L), and --use_cholesky (-c) parameters control the training options. A trained model can be saved with the --output_model_file (-M). If no training is desired at all, a model can be passed via the --input_model_file (-m) parameter.

The program can also provide predictions for test data using either the trained model or the given input model. Test points can be specified with the --test_file (-t) parameter. Predicted responses to the test points can be saved with the --output_predictions_file (-o) output parameter.

---

🍓 example time, bestie

For example, the following command trains a model on the data data.csv and responses responses.csv with lambda1 set to 0.4 and lambda2 set to 0 (so, LASSO is being solved), and then the model is saved to lasso_model.bin:

$ mlpack_lars --input_file data.csv --responses_file responses.csv
--lambda1 0.4 --lambda2 0 --output_model_file lasso_model.bin

The following command uses the lasso_model.bin to provide predicted responses for the data test.csv and save those responses to test_predictions.csv:

$ mlpack_lars --input_model_file lasso_model.bin --test_file test.csv
--output_predictions_file test_predictions.csv

ate, no crumbs. 👑

---

🎀 OPTIONAL INPUT OPTIONS 🎀

• --help (-h) [bool] — Default help info. your lil safety net 🫶
• --info [string] — Print help on a specific option. Default value ''.
• --input_file (-i) [unknown] — Matrix of covariates (X).
• --input_model_file (-m) [unknown] — Trained LARS model to use.
• --lambda1 (-l) [double] — Regularization parameter for l1-norm penalty. Default value 0.
• --lambda2 (-L) [double] — Regularization parameter for l2-norm penalty. Default value 0.
• --no_intercept (-n) [bool] — Do not fit an intercept in the model.
• --no_normalize (-N) [bool] — Do not normalize data to unit variance before modeling.
• --responses_file (-r) [unknown] — Matrix of responses/observations (y).
• --test_file (-t) [unknown] — Matrix containing points to regress on (test points).
• --use_cholesky (-c) [bool] — Use Cholesky decomposition during computation rather than explicitly computing the full Gram matrix. fancy math moment ✨
• --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_model_file (-M) [unknown] — Output LARS model.
• --output_predictions_file (-o) [unknown] — If --test_file is specified, this file is where the predicted responses will be saved.

---

🌈 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.

---

and that's the whole page, bestie 💖 this was mlpack-4.8.0, dated 22 June 2026, served up as mlpack_lars(1) — go solve some regression, main character 🌟