mlpack (version 4.6.0)

nbc: Parametric Naive Bayes Classifier

Description

An implementation of the Naive Bayes Classifier, used for classification. Given labeled data, an NBC model can be trained and saved, or, a pre-trained model can be used for classification.

Usage

nbc(
  incremental_variance = FALSE,
  input_model = NA,
  labels = NA,
  test = NA,
  training = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Value

A list with several components:

output_model

File to save trained Naive Bayes model to (NBCModel).

predictions

The matrix in which the predicted labels for the test set will be written (integer row).

probabilities

The matrix in which the predicted probability of labels for the test set will be written (numeric matrix).

Arguments

incremental_variance

The variance of each class will be calculated incrementally. Default value "FALSE" (logical).

input_model

Input Naive Bayes model (NBCModel).

labels

A file containing labels for the training set (integer row).

test

A matrix containing the test set (numeric matrix).

training

A matrix containing the training set (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 trains the Naive Bayes classifier on the given labeled training set, or loads a model from the given model file, and then may use that trained model to classify the points in a given test set.

The training set is specified with the "training" parameter. Labels may be either the last row of the training set, or alternately the "labels" parameter may be specified to pass a separate matrix of labels.

If training is not desired, a pre-existing model may be loaded with the "input_model" parameter.

The "incremental_variance" parameter can be used to force the training to use an incremental algorithm for calculating variance. This is slower, but can help avoid loss of precision in some cases.

If classifying a test set is desired, the test set may be specified with the "test" parameter, and the classifications may be saved with the "predictions"predictions parameter. If saving the trained model is desired, this may be done with the "output_model" output parameter.

Examples

# For example, to train a Naive Bayes classifier on the dataset "data" with
# labels "labels" and save the model to "nbc_model", the following command
# may be used:

if (FALSE) {
output <- nbc(training=data, labels=labels)
nbc_model <- output$output_model
}

# Then, to use "nbc_model" to predict the classes of the dataset "test_set"
# and save the predicted classes to "predictions", the following command may
# be used:

if (FALSE) {
output <- nbc(input_model=nbc_model, test=test_set)
predictions <- output$predictions
}