Can I optimize a custom performance metric?

anaRodrigues

Hi,
I want to generate a F beta score and change beta according to the weight I want to put on precision or recall. And then I would like to add it to a performance vector so I can optimize it. I know there's a 'Performance to data' operator, but what I would need is the reverse. Is there any way I can do this?

EDIT: I found a similar question, but no solution: https://community.rapidminer.com/discussion/38190/generate-own-performance-vector/p1

MartinLiebig

Hi @anaRodrigues ,

ehm, F1-Measure is already part of the operator Performance (Binominal)?

Anyway, you can use Performance to Data to get tp, fp, tn and fn and then calculate the F1 score by hand. Then you can use Extract Performance to get it as a performance vector again. Attached is an example.

Cheers,

Martin

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          <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.8.001" expanded="true" height="103" name="Decision Tree" width="90" x="45" y="34">
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          <connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Decision Tree" from_port="model" to_port="model"/>
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            <parameter key="true_negative" value="true"/>
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            <parameter key="specificity" value="false"/>
            <parameter key="youden" value="false"/>
            <parameter key="positive_predictive_value" value="false"/>
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            <parameter key="skip_undefined_labels" value="true"/>
            <parameter key="use_example_weights" value="true"/>
          </operator>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
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          <description align="left" color="blue" colored="true" height="103" resized="true" width="315" x="38" y="158">The model created in the Training step is applied to the current test set (10 %).&lt;br/&gt;The performance is evaluated and sent to the operator results.</description>
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        <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
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      <operator activated="true" class="extract_performance" compatibility="9.8.001" expanded="true" height="82" name="Performance (2)" width="90" x="1050" y="136">
        <parameter key="performance_type" value="data_value"/>
        <parameter key="statistics" value="average"/>
        <parameter key="attribute_name" value="F1"/>
        <parameter key="example_index" value="1"/>
        <parameter key="optimization_direction" value="maximize"/>
      </operator>
      <connect from_op="Retrieve Golf" from_port="output" to_op="Validation" to_port="example set"/>
      <connect from_op="Validation" from_port="performance 1" to_op="Performance to Data" to_port="performance vector"/>
      <connect from_op="Performance to Data" from_port="example set" to_op="Set Role" to_port="example set input"/>
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