mlpack_softmax_regression(1)

mlpack_softmax_regression 💅✨

okay bestie, so this is the manual page for mlpack_softmax_regression, part of the mlpack family of User Commands, and honestly? she's giving classifier royalty 👑

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✨ NAME ✨

mlpack_softmax_regression — softmax regression 🦋

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🌸 SYNOPSIS 🌸

mlpack_softmax_regression [-m unknown] [-l unknown] [-r double] [-n int] [-N bool] [-c int] [-T unknown] [-L unknown] [-t unknown] [-V bool] [-M unknown] [-p unknown] [-P unknown] [-h -v]

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💖 DESCRIPTION 💖

So this program does softmax regression — which is literally just logistic regression but it leveled up to handle the multiclass case, no cap. She also has support for L2 regularization, because we love a queen who avoids overfitting. The program can train a model, load an existing model, and give predictions (and optionally their accuracy) for test data. Versatile icon behavior only.

Training a softmax regression model is done by giving a file of training points with the --training_file (-t) parameter and their corresponding labels with the --labels_file (-l) parameter. The number of classes can be manually specified with the --number_of_classes (-c) parameter, and the maximum number of iterations of the L-BFGS optimizer can be specified with the --max_iterations (-n) parameter. The L2 regularization constant can be specified with the --lambda (-r) parameter and if an intercept term is not desired in the model, the --no_intercept (-N) parameter can be specified. 🎀

The trained model can be saved with the --output_model_file (-M) output parameter. If training is not desired, but only testing is, a model can be loaded with the --input_model_file (-m) parameter. At the current time, a loaded model cannot be trained further, so specifying both --input_model_file (-m) and --training_file (-t) is not allowed. Not me warning you twice, just... don't. 🙈

The program is also able to evaluate a model on test data. A test dataset can be specified with the --test_file (-T) parameter. Class predictions can be saved with the --predictions_file (-p) output parameter. If labels are specified for the test data with the --test_labels_file (-L) parameter, then the program will print the accuracy of the predictions on the given test set and its corresponding labels. Accuracy reveal, we love to see it ⭐

For example, to train a softmax regression model on the data dataset.csv with labels labels.csv with a maximum of 1000 iterations for training, saving the trained model to sr_model.bin, the following command can be used:

$ mlpack_softmax_regression --training_file dataset.csv
--labels_file labels.csv --output_model_file sr_model.bin

Then, to use sr_model.bin to classify the test points in test_points.csv, saving the output predictions to predictions.csv, the following command can be used:

$ mlpack_softmax_regression --input_model_file sr_model.bin
--test_file test_points.csv --predictions_file predictions.csv

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🎀 OPTIONAL INPUT OPTIONS 🎀

• --help (-h) [bool] — Default help info. The "what does anything do" button, bestie. 🫶
• --info [string] — Print help on a specific option. Default value ''.
• --input_model_file (-m) [unknown] — File containing existing model (parameters).
• --labels_file (-l) [unknown] — A matrix containing labels (0 or 1) for the points in the training set (y). The labels must order as a row. Yes, as a row, don't play with her.
• --lambda (-r) [double] — L2-regularization constant Default value 0.0001.
• --max_iterations (-n) [int] — Maximum number of iterations before termination. Default value 400. She will stop, periodt.
• --no_intercept (-N) [bool] — Do not add the intercept term to the model.
• --number_of_classes (-c) [int] — Number of classes for classification; if unspecified (or 0), the number of classes found in the labels will be used. Default value 0.
• --test_file (-T) [unknown] — Matrix containing test dataset.
• --test_labels_file (-L) [unknown] — Matrix containing test labels.
• --training_file (-t) [unknown] — A matrix containing the training set (the matrix of predictors, X).
• --verbose (-v) [bool] — Display informational messages and the full list of parameters and timers at the end of execution. Chatty main character energy 💬
• --version (-V) [bool] — Display the version of mlpack.

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🌈 OPTIONAL OUTPUT OPTIONS 🌈

• --output_model_file (-M) [unknown] — File to save trained softmax regression model to.
• --predictions_file (-p) [unknown] — Matrix to save predictions for test dataset into.
• --probabilities_file (-P) [unknown] — Matrix to save class probabilities for test dataset into.

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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, it's giving scholarly 💜

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fr fr: remember that you can't train a loaded model further, so don't pass --input_model_file (-m) and --training_file (-t) together. That's the one line of the group chat you can't ignore.

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okay that's the whole thing, you ate that 💅 and just so you know exactly what you're running: this is mlpack-4.8.0, dated 22 June 2026, and it's the mlpack_softmax_regression(1) page. slay, go classify something 🦋✨