mlpack (version 4.5.1)

softmax_regression: Softmax Regression

Description

An implementation of softmax regression for classification, which is a multiclass generalization of logistic regression. Given labeled data, a softmax regression model can be trained and saved for future use, or, a pre-trained softmax regression model can be used for classification of new points.

Usage

softmax_regression(
  input_model = NA,
  labels = NA,
  lambda = NA,
  max_iterations = NA,
  no_intercept = FALSE,
  number_of_classes = NA,
  test = NA,
  test_labels = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Value

A list with several components:

output_model

File to save trained softmax regression model to (SoftmaxRegression).

predictions

Matrix to save predictions for test dataset into (integer row).

probabilities

Matrix to save class probabilities for test dataset into (numeric matrix).

Arguments

input_model

File containing existing model (parameters) (SoftmaxRegression).

labels

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

lambda

L2-regularization constan. Default value "0.0001" (numeric).

max_iterations

Maximum number of iterations before termination. Default value "400" (integer).

no_intercept

Do not add the intercept term to the model. Default value "FALSE" (logical).

number_of_classes

Number of classes for classification; if unspecified (or 0), the number of classes found in the labels will be used. Default value "0" (integer).

test

Matrix containing test dataset (numeric matrix).

test_labels

Matrix containing test labels (integer row).

training

A matrix containing the training set (the matrix of predictors, X) (numeric matrix).

verbose

Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Author

mlpack developers

Details

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 the "training" parameter and their corresponding labels with the "labels" parameter. The number of classes can be manually specified with the "number_of_classes" parameter, and the maximum number of iterations of the L-BFGS optimizer can be specified with the "max_iterations" parameter. The L2 regularization constant can be specified with the "lambda" parameter and if an intercept term is not desired in the model, the "no_intercept" parameter can be specified.

The trained model can be saved with the "output_model" output parameter. If training is not desired, but only testing is, a model can be loaded with the "input_model" parameter. At the current time, a loaded model cannot be trained further, so specifying both "input_model" and "training" is not allowed.

The program is also able to evaluate a model on test data. A test dataset can be specified with the "test" parameter. Class predictions can be saved with the "predictions" output parameter. If labels are specified for the test data with the "test_labels" parameter, then the program will print the accuracy of the predictions on the given test set and its corresponding labels.

Examples

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

if (FALSE) {
output <- softmax_regression(training=dataset, labels=labels)
sr_model <- output$output_model
}

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

if (FALSE) {
output <- softmax_regression(input_model=sr_model, test=test_points)
predictions <- output$predictions
}