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  varunm1 Moderator, Member Posts: 1,207 Unicorn

  March 2019

  Hello @islem_h
  
  I tried with the polynomial dataset in RapidMiner Samples. I can get multiple rules using W-M5P or W-M5Rules operators from weka extension. I can also generate the tree (Screenshots below). It mainly depends on your data. If you can provide your XML process and dataset we can try to replicate and see what is happening. Sample code used attached in this as well. 
  
  
  

  <?xml version="1.0" encoding="UTF-8"?><process version="9.2.001">
    <context>
      <input/>
      <output/>
      <macros/>
    </context>
    <operator activated="true" class="process" compatibility="9.2.001" 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="9.2.001" expanded="true" height="68" name="Retrieve Polynomial" width="90" x="45" y="85">
          <parameter key="repository_entry" value="//Samples/data/Polynomial"/>
        </operator>
        <operator activated="true" class="split_data" compatibility="9.2.001" expanded="true" height="103" name="Split Data" width="90" x="179" y="85">
          <enumeration key="partitions">
            <parameter key="ratio" value="0.7"/>
            <parameter key="ratio" value="0.3"/>
          </enumeration>
          <parameter key="sampling_type" value="automatic"/>
          <parameter key="use_local_random_seed" value="false"/>
          <parameter key="local_random_seed" value="1992"/>
        </operator>
        <operator activated="false" class="weka:W-M5P" compatibility="7.3.000" expanded="true" height="82" name="W-M5P" width="90" x="380" y="442">
          <parameter key="N" value="false"/>
          <parameter key="U" value="false"/>
          <parameter key="R" value="false"/>
          <parameter key="M" value="4.0"/>
          <parameter key="L" value="false"/>
        </operator>
        <operator activated="true" class="weka:W-M5P" compatibility="7.3.000" expanded="true" height="82" name="W-M5P (2)" width="90" x="313" y="34">
          <parameter key="N" value="false"/>
          <parameter key="U" value="false"/>
          <parameter key="R" value="false"/>
          <parameter key="M" value="4.0"/>
          <parameter key="L" value="false"/>
        </operator>
        <operator activated="true" class="apply_model" compatibility="9.2.001" expanded="true" height="82" name="Apply Model" width="90" x="447" y="136">
          <list key="application_parameters"/>
          <parameter key="create_view" value="false"/>
        </operator>
        <operator activated="true" class="performance_regression" compatibility="9.2.001" expanded="true" height="82" name="Performance" width="90" x="514" y="34">
          <parameter key="main_criterion" value="first"/>
          <parameter key="root_mean_squared_error" value="true"/>
          <parameter key="absolute_error" value="false"/>
          <parameter key="relative_error" value="false"/>
          <parameter key="relative_error_lenient" value="false"/>
          <parameter key="relative_error_strict" value="false"/>
          <parameter key="normalized_absolute_error" value="false"/>
          <parameter key="root_relative_squared_error" value="false"/>
          <parameter key="squared_error" value="false"/>
          <parameter key="correlation" value="false"/>
          <parameter key="squared_correlation" value="false"/>
          <parameter key="prediction_average" value="false"/>
          <parameter key="spearman_rho" value="true"/>
          <parameter key="kendall_tau" value="false"/>
          <parameter key="skip_undefined_labels" value="true"/>
          <parameter key="use_example_weights" value="true"/>
        </operator>
        <connect from_op="Retrieve Polynomial" from_port="output" to_op="Split Data" to_port="example set"/>
        <connect from_op="Split Data" from_port="partition 1" to_op="W-M5P (2)" to_port="training set"/>
        <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model" to_port="unlabelled data"/>
        <connect from_op="W-M5P (2)" from_port="model" to_op="Apply Model" to_port="model"/>
        <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
        <connect from_op="Apply Model" from_port="model" to_port="result 2"/>
        <connect from_op="Performance" from_port="performance" 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"/>
        <portSpacing port="sink_result 3" spacing="0"/>
      </process>
    </operator>
  </process>
  

  Thanks for your understanding.

  
  

  Regards,
  Varun
  https://www.varunmandalapu.com/
  
  

  Be Safe. Follow precautions and Maintain Social Distancing

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  islem_h Member Posts: 19 Contributor I

  April 2019

  Thank you very much @varunm1 !
  It clarifies it all. 

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