"Decision tree with Greater than and Less than Leaf Nodes"

chobochobo Member Posts: 4 Contributor I
edited June 2019 in Help
Hi, I have a set of data that includes numerical data. I am trying to set it up so that the last nodes in the tree display outcomes that are greater than or less than a certain value. Which learners would I need to use for this?



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    SebastianLohSebastianLoh Member Posts: 99 Contributor II
    Hi chobo,

    the tree learner can handle numerical attributes. So you can just let the tree learner choose the (optimal) ranges. If you already know the thresholds you can discretize the data with the Discretize by User Specification.

    Take a look at this process:
    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.0">
      <operator activated="true" class="process" expanded="true" name="Process">
        <process expanded="true" height="314" width="614">
          <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
            <parameter key="repository_entry" value="//Samples/data/Golf"/>
          <operator activated="true" class="decision_tree" expanded="true" height="76" name="Decision Tree" width="90" x="246" y="30"/>
          <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve (2)" width="90" x="45" y="165">
            <parameter key="repository_entry" value="//Samples/data/Golf"/>
          <operator activated="true" class="discretize_by_user_specification" expanded="true" height="94" name="Discretize" width="90" x="246" y="165">
            <parameter key="attribute_filter_type" value="single"/>
            <parameter key="attribute" value="Humidity"/>
            <list key="classes">
              <parameter key="dry" value="68.0"/>
              <parameter key="nomal" value="75.0"/>
              <parameter key="humid" value="Infinity"/>
          <operator activated="true" class="decision_tree" expanded="true" height="76" name="Decision Tree (2)" width="90" x="447" y="165"/>
          <connect from_op="Retrieve" from_port="output" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Decision Tree" from_port="model" to_port="result 1"/>
          <connect from_op="Decision Tree" from_port="exampleSet" to_port="result 2"/>
          <connect from_op="Retrieve (2)" from_port="output" to_op="Discretize" to_port="example set input"/>
          <connect from_op="Discretize" from_port="example set output" to_op="Decision Tree (2)" to_port="training set"/>
          <connect from_op="Decision Tree (2)" from_port="model" to_port="result 3"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
          <portSpacing port="sink_result 3" spacing="0"/>
          <portSpacing port="sink_result 4" spacing="0"/>
    Ciao Sebastian
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