Provided by: mlpack-bin_3.0.4-1_amd64 bug


       mlpack_decision_tree - decision tree


        mlpack_decision_tree [-m unknown] [-l string] [-g double] [-n int] [-e bool] [-T string] [-L string] [-t string] [-V bool] [-w string] [-M unknown] [-p string] [-P string] [-h -v]


       Train  and  evaluate  using  a  decision  tree.  Given  a  dataset  containing  numeric or
       categorical features, and associated labels for each point in the  dataset,  this  program
       can train a decision tree on that data.

       The  training  set and associated labels are specified with the '--training_file (-t)' and
       '--labels_file (-l)' parameters, respectively. The labels  should  be  in  the  range  [0,
       num_classes  -  1].  Optionally,  if '--labels_file (-l)' is not specified, the labels are
       assumed to be the last dimension of the training dataset.

       When a model is trained, the '--output_model_file (-M)' output parameter may  be  used  to
       save the trained model. A model may be loaded for predictions with the '--input_model_file
       (-m)' parameter. The '--input_model_file (-m)' parameter may not  be  specified  when  the
       '--training_file  (-t)'  parameter  is specified. The '--minimum_leaf_size (-n)' parameter
       specifies the minimum number of training points that must fall into each leaf for it to be
       split.   The  '--minimum_gain_split  (-g)'  parameter  specifies  the minimum gain that is
       needed for the node to split. If '--print_training_error (-e)' is specified, the  training
       error will be printed.

       Test  data  may  be  specified  with  the '--test_file (-T)' parameter, and if performance
       numbers  are  desired  for  that  test   set,   labels   may   be   specified   with   the
       '--test_labels_file  (-L)' parameter. Predictions for each test point may be saved via the
       '--predictions_file (-p)' output parameter. Class probabilities for each prediction may be
       saved with the '--probabilities_file (-P)' output parameter.

       For  example,  to  train  a  decision  tree  with a minimum leaf size of 20 on the dataset
       contained in 'data.csv' with labels 'labels.csv', saving the output  model  to  'tree.bin'
       and printing the training error, one could call

       $  decision_tree  --training_file  data.arff  --labels_file labels.csv --output_model_file
       tree.bin --minimum_leaf_size 20 --minimum_gain_split 0.001 --print_training_error

       Then, to use that model to classify points in 'test_set.csv'  and  print  the  test  error
       given the labels 'test_labels.csv' using that model, while saving the predictions for each
       point to 'predictions.csv', one could call

       $ decision_tree --input_model_file tree.bin --test_file  test_set.arff  --test_labels_file
       test_labels.csv --predictions_file predictions.csv


       --help (-h) [bool]
              Default help info.

       --info [string]
              Get help on a specific module or option.  Default value ''.

       --input_model_file (-m) [unknown]
              Pre-trained decision tree, to be used with test points. Default value ''.

       --labels_file (-l) [string]
              Training labels. Default value ''.

       --minimum_gain_split (-g) [double]
              Minimum gain for node splitting. Default value 1e-07.

       --minimum_leaf_size (-n) [int]
              Minimum number of points in a leaf. Default value 20.

       --print_training_error (-e) [bool]
              Print the training error.

       --test_file (-T) [string]
              Testing dataset (may be categorical). Default value ''.

       --test_labels_file (-L) [string]
              Test point labels, if accuracy calculation is desired. 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.

       --version (-V) [bool]
              Display the version of mlpack.

       --weights_file (-w) [string] The weight of labels Default value ''.


       --output_model_file (-M) [unknown]
              Output for trained decision tree. Default value ''.

       --predictions_file (-p) [string]
              Class predictions for each test point. Default value ''.

       --probabilities_file (-P) [string]
              Class probabilities for each test point.  Default value ''.


       For further information, including relevant papers, citations,  and  theory,  consult  the
       documentation found at or included with your distribution of mlpack.