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how many nodes and depth in a decision decision tree?

sanisani Member Posts: 1 Newbie
I am  a newbie here :) . i am interested in knowing the number of nodes and depth of tree that is being built using the decision tree operator and apply model operator in rapidminer.

Is it possible that it can be shown in the results tab somewhere.

Answers

  • earmijoearmijo Member Posts: 265   Unicorn
    Not directly but you could use an operator named Get Decision Tree Path available in the extension Operator Toolbox (don't forget to install and load the Text Processing extension because it is a dependency). Take a look at the example below:

    ?xml version="1.0" encoding="UTF-8"?><process version="9.9.000">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="9.9.000" expanded="true" name="Process">
        <parameter key="logverbosity" value="init"/>
        <parameter key="random_seed" value="2001"/>
        <parameter key="send_mail" value="never"/>
        <parameter key="notification_email" value=""/>
        <parameter key="process_duration_for_mail" value="30"/>
        <parameter key="encoding" value="SYSTEM"/>
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="9.9.000" expanded="true" height="68" name="Retrieve Golf" width="90" x="112" y="187">
            <parameter key="repository_entry" value="//Samples/data/Golf"/>
          </operator>
          <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.9.000" expanded="true" height="103" name="Decision Tree" width="90" x="246" y="187">
            <parameter key="criterion" value="gain_ratio"/>
            <parameter key="maximal_depth" value="10"/>
            <parameter key="apply_pruning" value="true"/>
            <parameter key="confidence" value="0.1"/>
            <parameter key="apply_prepruning" value="true"/>
            <parameter key="minimal_gain" value="0.01"/>
            <parameter key="minimal_leaf_size" value="2"/>
            <parameter key="minimal_size_for_split" value="4"/>
            <parameter key="number_of_prepruning_alternatives" value="3"/>
          </operator>
          <operator activated="true" class="operator_toolbox:get_dectree_path" compatibility="2.10.000" expanded="true" height="82" name="Get Decision Tree Path" width="90" x="447" y="187"/>
          <operator activated="true" class="aggregate" compatibility="9.9.000" expanded="true" height="82" name="Aggregate" width="90" x="581" y="187">
            <parameter key="use_default_aggregation" value="false"/>
            <parameter key="attribute_filter_type" value="all"/>
            <parameter key="attribute" value=""/>
            <parameter key="attributes" value=""/>
            <parameter key="use_except_expression" value="false"/>
            <parameter key="value_type" value="attribute_value"/>
            <parameter key="use_value_type_exception" value="false"/>
            <parameter key="except_value_type" value="time"/>
            <parameter key="block_type" value="attribute_block"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="value_matrix_row_start"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="false"/>
            <parameter key="default_aggregation_function" value="average"/>
            <list key="aggregation_attributes">
              <parameter key="Path" value="count"/>
            </list>
            <parameter key="group_by_attributes" value="Path"/>
            <parameter key="count_all_combinations" value="false"/>
            <parameter key="only_distinct" value="false"/>
            <parameter key="ignore_missings" value="true"/>
          </operator>
          <connect from_op="Retrieve Golf" from_port="output" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Decision Tree" from_port="model" to_op="Get Decision Tree Path" to_port="mod"/>
          <connect from_op="Decision Tree" from_port="exampleSet" to_op="Get Decision Tree Path" to_port="exa"/>
          <connect from_op="Get Decision Tree Path" from_port="exa" to_op="Aggregate" to_port="example set input"/>
          <connect from_op="Aggregate" from_port="example set output" to_port="result 1"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
        </process>
      </operator>
    </process>

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