<root>
  <key>TrainRegression-dt</key>
  <exec>otbcli_TrainRegression</exec>
  <longname>TrainRegression (dt)</longname>
  <group>Learning</group>
  <description>Train a classifier from multiple images to perform regression.</description>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputImageList">ParameterMultipleInput</parameter_type>
    <key>io.il</key>
    <name>Input Image List</name>
    <description>A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict.</description>
    <datatype />
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
    <key>io.csv</key>
    <name>Input CSV file</name>
    <description>Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given</description>
    <isFolder />
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
    <key>io.imstat</key>
    <name>Input XML image statistics file</name>
    <description>Input XML file containing the mean and the standard deviation of the input images.</description>
    <isFolder />
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
    <key>io.out</key>
    <name>Output regression model</name>
    <description>Output file containing the model estimated (.txt format).</description>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
    <key>io.mse</key>
    <name>Mean Square Error</name>
    <description>Mean square error computed with the validation predictors</description>
    <minValue />
    <maxValue />
    <default>0.0</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>sample.mt</key>
    <name>Maximum training predictors</name>
    <description>Maximum number of training predictors (default = 1000) (no limit = -1).</description>
    <minValue />
    <maxValue />
    <default>1000</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>sample.mv</key>
    <name>Maximum validation predictors</name>
    <description>Maximum number of validation predictors (default = 1000) (no limit = -1).</description>
    <minValue />
    <maxValue />
    <default>1000</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
    <key>sample.vtr</key>
    <name>Training and validation sample ratio</name>
    <description>Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).</description>
    <minValue />
    <maxValue />
    <default>0.5</default>
    <optional>False</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>
