mlpack_nbc [-h] [-v] [-I] [-m string] [-l string] [-o string] [-M string] [-T string] [-t string] -V
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.
Labels are expected to be the last row of the training set (--train_file), but labels can also be passed in separately as their own file (--labels_file). If training is not desired, a pre-existing model can be loaded with the --input_model_file (-m) option.
The '--incremental_variance' option 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 should be in the file specified with the --test_file (-T) option, and the classifications will be saved to the file specified with the --output_file (-o) option. If saving a trained model is desired, the --output_model_file (-M) option should be given.
- --help (-h)
- Default help info.
- --incremental_variance (-I)
- The variance of each class will be calculated incrementally.
- --info [string]
- Get help on a specific module or option. Default value ''. --input_model_file (-m) [string] File containing input Naive Bayes model. Default value ''.
- --labels_file (-l) [string]
- A file containing labels for the training set. Default value ''.
- --output_file (-o) [string]
- The file in which the predicted labels for the test set will be written. Default value ''. --output_model_file (-M) [string] File to save trained Naive Bayes model to. Default value ''.
- --test_file (-T) [string]
- A file containing the test set. Default value ''. --training_file (-t) [string] A file containing the training set. Default value ''.
- --verbose (-v)
- Display informational messages and the full list of parameters and timers at the end of execution.
- --version (-V)
- Display the version of mlpack.
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.