SYNOPSIS

decision_stump [-h] [-v] -T string -t string [-b int] [-l string] [-o string] -V

DESCRIPTION

This program implements a decision stump, which is a single-level decision tree. The decision stump will split on one dimension of the input data, and will split into multiple buckets. The dimension and bins are selected by maximizing the information gain of the split. Optionally, the minimum number of training points in each bin can be specified with the --bin_size (-b) parameter.

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

This program allows training of a decision stump, and then application of the learned decision stump to a test dataset. To train a decision stump, a training dataset must be passed to --train_file (-t). Labels can either be present as the last dimension of the training dataset, or given explicitly with the --labels_file (-l) parameter.

A test file is given through the --test_file (-T) parameter. The predicted labels for the test set will be stored in the file specified by the --output_file (-o) parameter.

REQUIRED OPTIONS

--test_file (-T) [string]

A file containing the test set.

--train_file (-t) [string]

A file containing the training set.

OPTIONS

--bin_size (-b) [int]

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

--help (-h)

Default help info.

--info [string]

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

--labels_file (-l) [string]

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

--output_file (-o) [string]

The file in which the predicted labels for the test set will be written. Default value 'output.csv'.

--verbose (-v)

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

--version (-V)

Display the version of mlpack.

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.