# mlpack_softmax_regression

## NAME

mlpack_softmax_regression - softmax regression

## SYNOPSIS

mlpack_softmax_regression [-h] [-v]

## DESCRIPTION

This program performs softmax regression, a generalization of logistic regression to the multiclass case, and has support for L2 regularization. The program is able to train a model, load an existing model, and give predictions (and optionally their accuracy) for test data.

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

The trained model can be saved to a file with the --output_model_file (-m) option. If training is not desired, but only testing is, a model can be loaded with the --input_model_file (-i) option. At the current time, a loaded model cannot be trained further, so specifying both -i and -t is not allowed.

The program is also able to evaluate a model on test data. A test dataset can be specified with the --test_data (-T) option. Class predictions will be saved in the file specified with the --predictions_file (-p) option. If labels are specified for the test data, with the --test_labels (-L) option, then the program will print the accuracy of the predictions on the given test set and its corresponding labels.

## OPTIONAL INPUT OPTIONS

--help (-h) [bool]

Default help info. Default value 0.

--info [string]

Get help on a specific module or option. Default value ’’. --input_model_file (-m) [string] File containing existing model (parameters). Default value ’’.

--labels_file (-l) [string]

A matrix containing labels (0 or 1) for the points in the training set (y). The labels must order as a row. Default value ’’.

--lambda (-r) [double]

L2-regularization constant Default value 0.0001.

--max_iterations (-n) [int]

Maximum number of iterations before termination. Default value 400.

--no_intercept (-N) [bool]

Do not add the intercept term to the model. Default value 0. --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) [string]

Matrix containing test dataset. Default value ’’. --test_labels_file (-L) [string] Matrix containing test labels. Default value ’’. --training_file (-t) [string] A matrix containing the training set (the matrix of predictors, X). Default value ’’.

--verbose (-v) [bool]

Display informational messages and the full list of parameters and timers at the end of execution. Default value 0.

--version (-V) [bool]

Display the version of mlpack. Default value

0. |

## OPTIONAL OUTPUT OPTIONS

--output_model_file (-M) [string] File to save trained softmax regression model to. Default value ’’. --predictions_file (-p) [string] Matrix to save predictions for test dataset into. Default value ’’.

## ADDITIONAL INFORMATION

## ADDITIONAL INFORMATION

For further information, including relevant papers, citations, and theory, For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your consult the documentation found at http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK. DISTRIBUTION OF MLPACK.