NAME¶

mlpack_decision_tree - decision tree

SYNOPSIS¶

 mlpack_decision_tree [-m unknown] [-l unknown] [-D int] [-g double] [-n int] [-a bool] [-T string] [-L unknown] [-t string] [-V bool] [-w unknown] [-M unknown] [-p unknown] [-P unknown] [-h -v] 

DESCRIPTION¶

Train and evaluate using a decision tree. Given a dataset containing numeric or categorical features, and associated labels for each point in the dataset, this program can train a decision tree on that data.

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 '--minimum_gain_split (-g)' parameter specifies the minimum gain that is needed for the node to split. The '--maximum_depth (-D)' parameter specifies the maximum depth of the tree. If '--print_training_accuracy (-a)' is specified, the training accuracy will be printed.

Test data may be specified with the '--test_file (-T)' parameter, and if performance numbers are desired for that test set, labels 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 decision tree with a minimum leaf size of 20 on the dataset contained in 'data.csv' with labels 'labels.csv', saving the output model to 'tree.bin' and printing the training error, one could call

$ mlpack_decision_tree --training_file data.arff --labels_file labels.csv --output_model_file tree.bin --minimum_leaf_size 20 --minimum_gain_split 0.001 --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_decision_tree --input_model_file tree.bin --test_file test_set.arff --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 decision tree, to be used with test points. --labels_file (-l) [unknown] Training labels.

--maximum_depth (-D) [int]

Maximum depth of the tree (0 means no limit). Default value 0.

--minimum_gain_split (-g) [double]

Minimum gain for node splitting. Default value 1e-07.

--minimum_leaf_size (-n) [int]

Minimum number of points in a leaf. Default value 20.

--print_training_accuracy (-a) [bool]

Print the training accuracy.

--test_file (-T) [string]

Testing dataset (may be categorical).

--test_labels_file (-L) [unknown]

Test point labels, if accuracy calculation is desired.

--training_file (-t) [string]

Training dataset (may be categorical).

--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.

--weights_file (-w) [unknown]

The weight of labels

OPTIONAL OUTPUT OPTIONS¶

--output_model_file (-M) [unknown]

Output for trained decision tree.

--predictions_file (-p) [unknown]

Class predictions for each test point.

--probabilities_file (-P) [unknown]

Class probabilities for each test point.

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.