mlpack_random_forest

NAME

mlpack_random_forest - random forests

SYNOPSIS

mlpack_random_forest [-h] [-v]

DESCRIPTION

This program is an implementation of the standard random forest classification algorithm by Leo Breiman. A random forest can be trained and saved for later use, or a random forest may be loaded and predictions or class probabilities for points may be generated.

This documentation will be rewritten once #880 is merged.

OPTIONAL INPUT OPTIONS

--help (-h) [bool]

Default help info.

--info [string]

Get help on a specific module or option. Default value ’’. --input_model_file (-m) [unknown] Pre-trained random forest to use for classification. Default value ’’.

--labels_file (-l) [string]

Labels for training dataset. Default value ’’. --minimum_leaf_size (-n) [int] Minimum number of points in each leaf node. Default value 20.

--num_trees (-N) [int]

Number of trees in the random forest. Default value 10. --print_training_accuracy (-a) [bool] If set, then the accuracy of the model on the training set will be predicted (verbose must also be specified).

--test_file (-T) [string]

Test dataset to produce predictions for. Default value ’’. --test_labels_file (-L) [string] Test dataset labels, if accuracy calculation is desired. Default value ’’. --training_file (-t) [string] Training dataset. Default value ’’.

--verbose (-v) [bool]

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

--version (-V) [bool]

Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

--output_model_file (-M) [unknown] Model to save trained random forest to. Default value ’’. --predictions_file (-p) [string] Predicted classes for each point in the test set. Default value ’’. --probabilities_file (-P) [string] Predicted class probabilities for each point in the test set. 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.