mlpack_random_forest

NAME

mlpack_random_forest - random forests

SYNOPSIS

mlpack_random_forest [-m unknown] [-l string] [-n int] [-N int] [-a bool] [-T string] [-L string] [-t string] [-V bool] [-M unknown] [-p string] [-P string] [-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

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