.\" Text automatically generated by txt2man
.TH mlpack_decision_tree 1 "18 November 2018" "mlpack-3.0.4" "User Commands"
.SH NAME
\fBmlpack_decision_tree \fP- decision tree
.SH SYNOPSIS
.nf
.fam C
 \fBmlpack_decision_tree\fP [\fB-m\fP \fIunknown\fP] [\fB-l\fP \fIstring\fP] [\fB-g\fP \fIdouble\fP] [\fB-n\fP \fIint\fP] [\fB-e\fP \fIbool\fP] [\fB-T\fP \fIstring\fP] [\fB-L\fP \fIstring\fP] [\fB-t\fP \fIstring\fP] [\fB-V\fP \fIbool\fP] [\fB-w\fP \fIstring\fP] [\fB-M\fP \fIunknown\fP] [\fB-p\fP \fIstring\fP] [\fB-P\fP \fIstring\fP] [\fB-h\fP \fB-v\fP] 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


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.
.PP
The training set and associated labels are specified with the '\fB--training_file\fP
(\fB-t\fP)' and '\fB--labels_file\fP (\fB-l\fP)' parameters, respectively. The labels should be
in the range [0, num_classes - 1]. Optionally, if '\fB--labels_file\fP (\fB-l\fP)' is not
specified, the labels are assumed to be the last dimension of the training
dataset.
.PP
When a model is trained, the '\fB--output_model_file\fP (\fB-M\fP)' output parameter may
be used to save the trained model. A model may be loaded for predictions with
the '\fB--input_model_file\fP (\fB-m\fP)' parameter. The '\fB--input_model_file\fP (\fB-m\fP)'
parameter may not be specified when the '\fB--training_file\fP (\fB-t\fP)' parameter is
specified. The '\fB--minimum_leaf_size\fP (\fB-n\fP)' parameter specifies the minimum
number of training points that must fall into each leaf for it to be split. 
The '\fB--minimum_gain_split\fP (\fB-g\fP)' parameter specifies the minimum gain that is
needed for the node to split. If '\fB--print_training_error\fP (\fB-e\fP)' is specified,
the training error will be printed.
.PP
Test data may be specified with the '\fB--test_file\fP (\fB-T\fP)' parameter, and if
performance numbers are desired for that test set, labels may be specified
with the '\fB--test_labels_file\fP (\fB-L\fP)' parameter. Predictions for each test point
may be saved via the '\fB--predictions_file\fP (\fB-p\fP)' output parameter. Class
probabilities for each prediction may be saved with the '\fB--probabilities_file\fP
(\fB-P\fP)' output parameter.
.PP
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
.PP
$ decision_tree \fB--training_file\fP data.arff \fB--labels_file\fP labels.csv
\fB--output_model_file\fP tree.bin \fB--minimum_leaf_size\fP 20 \fB--minimum_gain_split\fP
0.001
\fB--print_training_error\fP
.PP
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 
.PP
$ decision_tree \fB--input_model_file\fP tree.bin \fB--test_file\fP test_set.arff
\fB--test_labels_file\fP test_labels.csv \fB--predictions_file\fP predictions.csv
.RE
.PP

.SH OPTIONAL INPUT OPTIONS 

.TP
.B
\fB--help\fP (\fB-h\fP) [\fIbool\fP]
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Get help on a specific module or option.  Default value ''. 
.TP
.B
\fB--input_model_file\fP (\fB-m\fP) [\fIunknown\fP]
Pre-trained decision tree, to be used with test points. Default value ''. 
.TP
.B
\fB--labels_file\fP (\fB-l\fP) [\fIstring\fP]
Training labels. Default value ''. 
.TP
.B
\fB--minimum_gain_split\fP (\fB-g\fP) [\fIdouble\fP]
Minimum gain for node splitting. Default value 1e-07. 
.TP
.B
\fB--minimum_leaf_size\fP (\fB-n\fP) [\fIint\fP]
Minimum number of points in a leaf. Default value 20. 
.TP
.B
\fB--print_training_error\fP (\fB-e\fP) [\fIbool\fP]
Print the training error. 
.TP
.B
\fB--test_file\fP (\fB-T\fP) [\fIstring\fP]
Testing dataset (may be categorical). Default value ''. 
.TP
.B
\fB--test_labels_file\fP (\fB-L\fP) [\fIstring\fP]
Test point labels, if accuracy calculation is desired. Default value ''. 
.TP
.B
\fB--training_file\fP (\fB-t\fP) [\fIstring\fP]
Training dataset (may be categorical). Default value ''. 
.TP
.B
\fB--verbose\fP (\fB-v\fP) [\fIbool\fP]
Display informational messages and the full list of parameters and timers at the end of execution. 
.TP
.B
\fB--version\fP (\fB-V\fP) [\fIbool\fP]
Display the version of mlpack. 
.TP
.B
\fB--weights_file\fP (\fB-w\fP) [\fIstring\fP] The weight of labels Default value ''.
.SH OPTIONAL OUTPUT OPTIONS 

.TP
.B
\fB--output_model_file\fP (\fB-M\fP) [\fIunknown\fP]
Output for trained decision tree. Default value ''. 
.TP
.B
\fB--predictions_file\fP (\fB-p\fP) [\fIstring\fP]
Class predictions for each test point. Default value ''. 
.TP
.B
\fB--probabilities_file\fP (\fB-P\fP) [\fIstring\fP]
Class probabilities for each test point.  Default value ''.
.SH ADDITIONAL INFORMATION

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.