NAME¶

SYNOPSIS¶

 mlpack_decision_stump [-b int] [-m unknown] [-l string] [-T string] [-t string] [-V bool] [-M unknown] [-p string] [-h -v] 

DESCRIPTION¶

The decision stump is parameterized by a splitting dimension and a vector of values that denote the splitting values of each bin.

This program enables several applications: a decision tree may be trained or loaded, and then that decision tree may be used to classify a given set of test points. The decision tree may also be saved to a file for later usage.

To train a decision stump, training data should be passed with the ’--training_file (-t)' parameter, and their corresponding labels should be passed with the '--labels_file (-l)' option. Optionally, if '--labels_file (-l)' is not specified, the labels are assumed to be the last dimension of the training dataset. The '--bucket_size (-b)' parameter controls the minimum number of training points in each decision stump bucket.

For classifying a test set, a decision stump may be loaded with the ’--input_model_file (-m)' parameter (useful for the situation where a stump has already been trained), and a test set may be specified with the ’--test_file (-T)' parameter. The predicted labels can be saved with the ’--predictions_file (-p)' output parameter.

Because decision stumps are trained in batch, retraining does not make sense and thus it is not possible to pass both '--training_file (-t)' and ’--input_model_file (-m)'; instead, simply build a new decision stump with the training data.

After training, a decision stump can be saved with the '--output_model_file (-M)' output parameter. That stump may later be re-used in subsequent calls to this program (or others).

OPTIONAL INPUT OPTIONS¶

--bucket_size (-b) [int]

The minimum number of training points in each decision stump bucket. Default value 6.

--help (-h) [bool]

Default help info.

--info [string]

Get help on a specific module or option. Default value ''.

--input_model_file (-m) [unknown]

Decision stump model to load. Default value ''.

--labels_file (-l) [string]

Labels for the training set. If not specified, the labels are assumed to be the last row of the training data. Default value ''.

--test_file (-T) [string]

A dataset to calculate predictions for. Default value ''.

--training_file (-t) [string]

The dataset to train on. 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]

Output decision stump model to save. Default value ''.

--predictions_file (-p) [string]

The output matrix that will hold the predicted labels for the test set. Default value ''.

ADDITIONAL INFORMATION¶