<root>
  <key>TrainVectorClassifier-dt</key>
  <exec>otbcli_TrainVectorClassifier</exec>
  <longname>TrainVectorClassifier (dt)</longname>
  <group>Learning</group>
  <description>Train a classifier based on labeled geometries and a list of features to consider.</description>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputVectorDataList">ParameterMultipleInput</parameter_type>
    <key>io.vd</key>
    <name>Input Vector Data</name>
    <description>Input geometries used for training (note : all geometries from the layer will be used)</description>
    <datatype />
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
    <key>io.stats</key>
    <name>Input XML image statistics file</name>
    <description>XML file containing mean and variance of each feature.</description>
    <isFolder />
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
    <key>io.confmatout</key>
    <name>Output confusion matrix</name>
    <description>Output file containing the confusion matrix (.csv format).</description>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
    <key>io.out</key>
    <name>Output model</name>
    <description>Output file containing the model estimated (.txt format).</description>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_StringList">ParameterString</parameter_type>
    <key>feat</key>
    <name>Field names for training features.</name>
    <description>List of field names in the input vector data to be used as features for training.</description>
    <options />
    <default />
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_String">ParameterString</parameter_type>
    <key>cfield</key>
    <name>Field containing the class id for supervision</name>
    <description>Field containing the class id for supervision. Only geometries with this field available will be taken into account.</description>
    <default>class</default>
    <multiline />
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>layer</key>
    <name>Layer Index</name>
    <description>Index of the layer to use in the input vector file.</description>
    <minValue />
    <maxValue />
    <default>0</default>
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputVectorDataList">ParameterMultipleInput</parameter_type>
    <key>valid.vd</key>
    <name>Validation Vector Data</name>
    <description>Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used)</description>
    <datatype />
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>valid.layer</key>
    <name>Layer Index</name>
    <description>Index of the layer to use in the validation vector file.</description>
    <minValue />
    <maxValue />
    <default>0</default>
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
    <key>classifier</key>
    <name>Classifier to use for the training</name>
    <description>Choice of the classifier to use for the training.</description>
    <options>
      <choices>
        <choice>dt</choice>
        </choices>
    </options>
    <default>0</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>classifier.dt.max</key>
    <name>Maximum depth of the tree</name>
    <description>The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned.</description>
    <minValue />
    <maxValue />
    <default>65535</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>classifier.dt.min</key>
    <name>Minimum number of samples in each node</name>
    <description>If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split.</description>
    <minValue />
    <maxValue />
    <default>10</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
    <key>classifier.dt.ra</key>
    <name>Termination criteria for regression tree</name>
    <description />
    <minValue />
    <maxValue />
    <default>0.01</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>classifier.dt.cat</key>
    <name>Cluster possible values of a categorical variable into K &lt;= cat clusters to find a suboptimal split</name>
    <description>Cluster possible values of a categorical variable into K &lt;= cat clusters to find a suboptimal split.</description>
    <minValue />
    <maxValue />
    <default>10</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>classifier.dt.f</key>
    <name>K-fold cross-validations</name>
    <description>If cv_folds &gt; 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds.</description>
    <minValue />
    <maxValue />
    <default>10</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
    <key>classifier.dt.r</key>
    <name>Set Use1seRule flag to false</name>
    <description>If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate.</description>
    <default>True</default>
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
    <key>classifier.dt.t</key>
    <name>Set TruncatePrunedTree flag to false</name>
    <description>If true, then pruned branches are physically removed from the tree.</description>
    <default>True</default>
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>rand</key>
    <name>set user defined seed</name>
    <description>Set specific seed. with integer value.</description>
    <minValue />
    <maxValue />
    <default>0</default>
    <optional>True</optional>
  </parameter>
</root>
