mlpack_hoeffding_tree

NAME

mlpack_hoeffding_tree - hoeffding trees

SYNOPSIS

mlpack_hoeffding_tree [-h] [-v]

DESCRIPTION

This program implements Hoeffding trees, a form of streaming decision tree suited best for large (or streaming) datasets. This program supports both categorical and numeric data stored in the ARFF format. Given an input dataset, this program is able to train the tree with numerous training options, and save the model to a file. The program is also able to use a trained model or a model from file in order to predict classes for a given test set.

The training file and associated labels are specified with the --training_file and --labels_file options, respectively. The training file must be in ARFF format. The training may be performed in batch mode (like a typical decision tree algorithm) by specifying the --batch_mode option, but this may not be the best option for large datasets.

When a model is trained, it may be saved to a file with the --output_model_file (-M) option. A model may be loaded from file for further training or testing with the --input_model_file (-m) option.

A test file may be specified with the --test_file (-T) option, and if performance numbers are desired for that test set, labels may be specified with the --test_labels_file (-L) option. Predictions for each test point will be stored in the file specified by --predictions_file (-p) and probabilities for each predictions will be stored in the file specified by the --probabilities_file (-P) option.

OPTIONAL INPUT OPTIONS

--batch_mode (-b) [bool]

If true, samples will be considered in batch instead of as a stream. This generally results in better trees but at the cost of memory usage and runtime. Default value 0.

--bins (-B) [int]

If the ’domingos’ split strategy is used, this specifies the number of bins for each numeric split. Default value 10.

--confidence (-c) [double]

Confidence before splitting (between 0 and 1). Default value 0.95.

--help (-h) [bool]

Default help info. Default value 0.

--info [string]

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

--info_gain (-i) [bool]

If set, information gain is used instead of Gini impurity for calculating Hoeffding bounds. Default value 0. --input_model_file (-m) [string] Input trained Hoeffding tree model. Default value ’’.

--labels_file (-l) [string]

Labels for training dataset. Default value ’’.

--max_samples (-n) [int]

Maximum number of samples before splitting. Default value 5000.

--min_samples (-I) [int]

Minimum number of samples before splitting. Default value 100. --numeric_split_strategy (-N) [string] The splitting strategy to use for numeric features: ’domingos’ or ’binary’. Default value ’binary’. --observations_before_binning (-o) [int] If the ’domingos’ split strategy is used, this specifies the number of samples observed before binning is performed. Default value 100.

--passes (-s) [int]

Number of passes to take over the dataset. Default value 1.

--test_file (-T) [string]

Testing dataset (may be categorical). Default value ’’. --test_labels_file (-L) [string] Labels of test data. Default value ’’. --training_file (-t) [string] Training dataset (may be categorical). Default value ’’.

--verbose (-v) [bool]

Display informational messages and the full list of parameters and timers at the end of execution. Default value 0.

--version (-V) [bool]

Display the version of mlpack. Default value

0.

OPTIONAL OUTPUT OPTIONS

--output_model_file (-M) [string] Output for trained Hoeffding tree model. Default value ’’. --predictions_file (-p) [string] Matrix to output label predictions for test data into. Default value ’’. --probabilities_file (-P) [string] In addition to predicting labels, provide rediction probabilities in this matrix. Default value ’’.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

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