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  lionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 1,195 Unicorn

  March 2019 Solution Accepted

  I think you can use the Sample operator and define the ratio of your 2 classes you want to obtain.
  
  Regards
  
  Lionel. 

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  varunm1 Moderator, Member Posts: 1,207 Unicorn

  March 2019 edited March 2019 Solution Accepted

  @lionelderkrikor mentioned well in his answer. You can use "SMOTE upsampling", this will give you data with an equal number of classes. Sample below for titanic dataset where samples labeled are Yes (349) and No (547). Now once SMOTE operator is applied it gives each class 567 samples. XML code below, to use XML you should copy this code and go to View --> Show Panel --> XML. Paste this code in the XML window and click on the green tick mark, you can see the process.
  

  <?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 Titanic Training" width="90" x="45" y="136">
          <parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
        </operator>
        <operator activated="true" class="operator_toolbox:smote" compatibility="1.8.000" expanded="true" height="82" name="SMOTE Upsampling" width="90" x="313" y="136">
          <parameter key="number_of_neighbours" value="5"/>
          <parameter key="normalize" value="true"/>
          <parameter key="equalize_classes" value="true"/>
          <parameter key="upsampling_size" value="1000"/>
          <parameter key="auto_detect_minority_class" value="true"/>
          <parameter key="round_integers" value="true"/>
          <parameter key="nominal_change_rate" value="0.5"/>
          <parameter key="use_local_random_seed" value="false"/>
          <parameter key="local_random_seed" value="1992"/>
        </operator>
        <connect from_op="Retrieve Titanic Training" from_port="output" to_op="SMOTE Upsampling" to_port="exa"/>
        <connect from_op="SMOTE Upsampling" from_port="ups" 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>
  

  In general, we work with imbalanced data rather than sampling, this is because we need to deal with this sort of data in real-world settings. If sampling is good in your work, then you can follow the aforementioned process.
  
  Hope this helps.
  
  

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

  Be Safe. Follow precautions and Maintain Social Distancing

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