Man page - mlpack_random_forest(1)
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apt-get install mlpack-bin
Manual
mlpack_random_forest
NAMESYNOPSIS
DESCRIPTION
OPTIONAL INPUT OPTIONS
OPTIONAL OUTPUT OPTIONS
ADDITIONAL INFORMATION
NAME
mlpack_random_forest - random forests
SYNOPSIS
mlpack_random_forest [ -m unknown ] [ -l unknown ] [ -D int ] [ -g double ] [ -n int ] [ -N int ] [ -a bool ] [ -s int ] [ -d int ] [ -T unknown ] [ -L unknown ] [ -t unknown ] [ -V bool ] [ -w bool ] [ -M unknown ] [ -p unknown ] [ -P unknown ] [ -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.
The training set and associated labels are specified with the ’ --training_file ( -t )’ and ’ --labels_file ( -l )’ parameters, respectively. The labels should be in the range ‘[0, num_classes - 1]‘. Optionally, if ’ --labels_file ( -l )’ is not specified, the labels are assumed to be the last dimension of the training dataset.
When a model is trained, the ’ --output_model_file ( -M )’ output parameter may be used to save the trained model. A model may be loaded for predictions with the ’ --input_model_file ( -m )’parameter. The ’ --input_model_file ( -m )’ parameter may not be specified when the ’ --training_file ( -t )’ parameter is specified. The ’ --minimum_leaf_size ( -n )’ parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The ’ --num_trees ( -N )’ controls the number of trees in the random forest. The ’ --minimum_gain_split ( -g )’ parameter controls the minimum required gain for a decision tree node to split. Larger values will force higher-confidence splits. The ’ --maximum_depth ( -D )’ parameter specifies the maximum depth of the tree. The ’ --subspace_dim ( -d )’ parameter is used to control the number of random dimensions chosen for an individual node’s split. If ’ --print_training_accuracy ( -a )’ is specified, the calculated accuracy on the training set will be printed.
Test data may be specified with the ’ --test_file ( -T )’ parameter, and if performance measures are desired for that test set, labels for the test points may be specified with the ’ --test_labels_file ( -L )’ parameter. Predictions for each test point may be saved via the ’ --predictions_file ( -p )’output parameter. Class probabilities for each prediction may be saved with the ’ --probabilities_file ( -P )’ output parameter.
For example, to train a random forest with a minimum leaf size of 20 using 10 trees on the dataset contained in ’data.csv’with labels ’labels.csv’, saving the output random forest to ’rf_model.bin’ and printing the training error, one could call
$ mlpack_random_forest --training_file data.csv --labels_file labels.csv --minimum_leaf_size 20 --num_trees 10 --output_model_file rf_model.bin --print_training_accuracy
Then, to use that model to classify points in ’test_set.csv’ and print the test error given the labels ’test_labels.csv’ using that model, while saving the predictions for each point to ’predictions.csv’, one could call
$ mlpack_random_forest --input_model_file rf_model.bin --test_file test_set.csv --test_labels_file test_labels.csv --predictions_file predictions.csv
OPTIONAL INPUT OPTIONS
--help (-h) [ bool ]
Default help info.
--info [string]
Print help on a specific option. Default value ’’.
--input_model_file (-m) [ unknown ]
Pre-trained random forest to use for classification. --labels_file ( -l ) [ unknown ] Labels for training dataset.
--maximum_depth (-D) [ int ]
Maximum depth of the tree (0 means no limit). Default value 0.
--minimum_gain_split (-g) [ double ]
Minimum gain needed to make a split when building a tree. Default value 0.
--minimum_leaf_size (-n) [ int ]
Minimum number of points in each leaf node. Default value 1.
--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).
--seed (-s) [ int ]
Random seed. If 0, ’std::time(NULL)’ is used. Default value 0.
--subspace_dim (-d) [ int ]
Dimensionality of random subspace to use for each split. ’0’ will autoselect the square root of data dimensionality. Default value 0.
--test_file (-T) [ unknown ]
Test dataset to produce predictions for.
--test_labels_file (-L) [ unknown ]
Test dataset labels, if accuracy calculation is desired.
--training_file (-t) [ unknown ]
Training dataset.
--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.
--warm_start (-w) [ bool ]
If true and passed along with ‘training‘ and ‘input_model‘ then trains more trees on top of existing model.
OPTIONAL OUTPUT OPTIONS
--output_model_file (-M) [ unknown ]
Model to save trained random forest to.
--predictions_file (-p) [ unknown ]
Predicted classes for each point in the test set.
--probabilities_file (-P) [ unknown ]
Predicted class probabilities for each point in the test set.
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.