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Remove correlated features from training set and apply the same features to test set

Andy3Andy3 Member Posts: 7 Newbie
edited April 2020 in Help
Hello all,

I just wondering how you achieve to remove pairwise correlated features from your training set (using the Remove Correlated Attributes operator) and apply the same features to your test set? If I should compare this operation to something I think about the "Apply feature set" (as exists for the features selection operator) or somewhat OHE and the Preprocessing model output. See screenshot below of the process. I have normally these two training and test preprocessing operations in two different processes.

Thanks for your help.
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Best Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,128  RM Data Scientist
    Solution Accepted
    Hi @Andy3 ,
    if you need to do it, you can use Data to Weights for it. Attached is an example.

    BR,
    Martin

    <?xml version="1.0" encoding="UTF-8"?><process version="9.6.000">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="9.6.000" 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="9.6.000" expanded="true" height="68" name="Retrieve Sonar" width="90" x="45" y="34">
            <parameter key="repository_entry" value="//Samples/data/Sonar"/>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="9.6.000" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">
            <parameter key="attribute_filter_type" value="single"/>
            <parameter key="attribute" value="attribute_1"/>
            <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"/>
          </operator>
          <operator activated="true" class="data_to_weights" compatibility="9.6.000" expanded="true" height="82" name="Data to Weights" width="90" x="313" y="34">
            <parameter key="normalize_weights" value="false"/>
            <parameter key="sort_weights" value="true"/>
            <parameter key="sort_direction" value="ascending"/>
          </operator>
          <operator activated="true" class="retrieve" compatibility="9.6.000" expanded="true" height="68" name="Retrieve Sonar (2)" width="90" x="45" y="238">
            <parameter key="repository_entry" value="//Samples/data/Sonar"/>
          </operator>
          <operator activated="true" class="select_by_weights" compatibility="9.6.000" expanded="true" height="103" name="Select by Weights" width="90" x="514" y="136">
            <parameter key="weight_relation" value="greater equals"/>
            <parameter key="weight" value="1.0"/>
            <parameter key="k" value="10"/>
            <parameter key="p" value="0.5"/>
            <parameter key="deselect_unknown" value="true"/>
            <parameter key="use_absolute_weights" value="true"/>
          </operator>
          <connect from_op="Retrieve Sonar" from_port="output" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select Attributes" from_port="example set output" to_op="Data to Weights" to_port="example set"/>
          <connect from_op="Data to Weights" from_port="weights" to_op="Select by Weights" to_port="weights"/>
          <connect from_op="Retrieve Sonar (2)" from_port="output" to_op="Select by Weights" to_port="example set input"/>
          <connect from_op="Select by Weights" 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>


    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
    Andy3sgenzer

Answers

  • Andy3Andy3 Member Posts: 7 Newbie
    Yeah, fair enough though I was hoping the where an operator(s) like this so I could have consistency through the various data sets (and in my mind :smile: ). I leave it there.

    Thanks for the help.
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