mlpack_decision_stump - decision stump

       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.

mlpack-3.4.2                                      11 April 2022                         mlpack_decision_stump(1)

       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)' 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).

mlpack_decision_stump - decision stump

--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]
              Print help on a specific option. Default value ''.

       --input_model_file(-m)[unknown]
              Decision stump model to load.

       --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.

       --test_file(-T)[string]
              A dataset to calculate predictions for.

       --training_file(-t)[string]
              The dataset to train on.

       --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.

--output_model_file(-M)[unknown]
              Output decision stump model to save.

       --predictions_file(-p)[string]
              The output matrix that will hold the predicted labels for the test set.

mlpack_decision_stump [-bint] [-munknown] [-lstring] [-Tstring] [-tstring] [-Vbool] [-Munknown] [-pstring] [-h-v]