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least square deviation

hadiyaghoobihadiyaghoobi Member Posts: 2 Learner I
in Help
hello all.

How to calculate the output of the card decision tree algorithm with the least square deviation criterion?

Answers

  • IngoRMIngoRM Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Community Manager, RMResearcher, Member, University Professor Posts: 1,751 RM Founder
    Hi,
    You can use the operator "Decision Tree" for this.  All you need to do is to change the parameter value for "criterion" to "least squares".  The process below shows a simple example for this.
    More information about decision trees can be found here: https://academy.rapidminer.com/catalog?label=search&value=decision trees
    Hope this helps,
    Ingo

    <?xml version="1.0" encoding="UTF-8"?><process version="9.2.000"><br>  <context><br>    <input/><br>    <output/><br>    <macros/><br>  </context><br>  <operator activated="true" class="process" compatibility="9.2.000" expanded="true" name="Process"><br>    <parameter key="logverbosity" value="init"/><br>    <parameter key="random_seed" value="2001"/><br>    <parameter key="send_mail" value="never"/><br>    <parameter key="notification_email" value=""/><br>    <parameter key="process_duration_for_mail" value="30"/><br>    <parameter key="encoding" value="UTF-8"/><br>    <process expanded="true"><br>      <operator activated="true" class="retrieve" compatibility="9.2.000" expanded="true" height="68" name="Retrieve Polynomial" width="90" x="45" y="34"><br>        <parameter key="repository_entry" value="//Samples/data/Polynomial"/><br>      </operator><br>      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.2.000" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="34"><br>        <parameter key="criterion" value="least_square"/><br>        <parameter key="maximal_depth" value="10"/><br>        <parameter key="apply_pruning" value="true"/><br>        <parameter key="confidence" value="0.1"/><br>        <parameter key="apply_prepruning" value="true"/><br>        <parameter key="minimal_gain" value="0.01"/><br>        <parameter key="minimal_leaf_size" value="2"/><br>        <parameter key="minimal_size_for_split" value="4"/><br>        <parameter key="number_of_prepruning_alternatives" value="3"/><br>      </operator><br>      <connect from_op="Retrieve Polynomial" from_port="output" to_op="Decision Tree" to_port="training set"/><br>      <connect from_op="Decision Tree" from_port="model" to_port="result 1"/><br>      <portSpacing port="source_input 1" spacing="0"/><br>      <portSpacing port="sink_result 1" spacing="0"/><br>      <portSpacing port="sink_result 2" spacing="0"/><br>    </process><br>  </operator><br></process><br>



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