xenial (1) mlpack_decision_stump.1.gz

NAME

       mlpack_decision_stump - decision stump

SYNOPSIS

        mlpack_decision_stump [-h] [-v] [-b int] [-m string] [-l string] [-M string] [-p string] [-T string] [-t 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 --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).

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 ''.  --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 ’predictions.csv'.

       --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.

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(1)