NAME

       mlpack_decision_stump - decision stump

SYNOPSIS

        mlpack_decision_stump [-h] [-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 --bucket_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 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)
       option,  and  their  corresponding  labels  should  be  passed with the --labels_file (-l)
       option. Optionally, if --labels_file 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 not just been trained), and a
       test set may be specified with the --test_file (-T) parameter. The predicted  labels  will
       be saved to the file specified with the --predictions_file (-p) parameter.

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

       A trained decision stump can be saved with the --output_model_file (-M) option. 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)
              Default help info.

       --info [string]
              Get  help  on  a  specific module or option.  Default value ''.  --input_model_file
              (-m) [string] File containing decision stump model to load.  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 ’'.

       --test_file (-T) [string]
              A  file containing the test set. Default value ’'.  --training_file (-t) [string] A
              file containing the training set. Default value ''.

       --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.

OPTIONAL OUTPUT OPTIONS

       --output_model_file  (-M)  [string] File to save trained decision stump model to.  Default
       value ''.  --predictions_file (-p) [string] The file in which the predicted labels for the
       test set will be written. 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.

                                                          mlpack_decision_stump(16 November 2017)