"Probability Outputs of Logistic Regression (kernalized)"

confuzio
confuzio New Altair Community Member
edited November 5 in Community Q&A
Hello,

using Kernel Logistic Regression in RapidMiner 5.1 I could not figure out by now how to get probability predictions (the estimated probability that this customer will default is e.g. 0.11, not simply: this customer will default). When using "apply model" all I get is 0/1-predictions (RM internally uses threshold p_hat = 0.5?).
As I use the radial kernel I can not simply plug the estimated coefficients in p_hat = exp(Xb)/(1+exp(Xb)).

Can anyone tell my how to get probability predictions? I'd be really grateful!

Here is my process (As I usually work with R I'm not really used to RapidMiner by now): 

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.1.001" expanded="true" name="Process">
    <process expanded="true" height="404" width="592">
      <operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Retrieve" width="90" x="45" y="75">
        <parameter key="repository_entry" value="Data"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.1.001" expanded="true" height="76" name="Set Role" width="90" x="179" y="75">
        <parameter key="name" value="default"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="optimize_parameters_grid" compatibility="5.1.001" expanded="true" height="94" name="Optimize Parameters (Grid)" width="90" x="313" y="75">
        <list key="parameters">
          <parameter key="Logistic Regression (2).kernel_gamma" value="[0.0001;2000;5;logarithmic]"/>
          <parameter key="Logistic Regression (2).C" value="[0.00000001;10;5;logarithmic]"/>
        </list>
        <process expanded="true" height="381" width="592">
          <operator activated="true" class="x_validation" compatibility="5.1.001" expanded="true" height="112" name="Validation (2)" width="90" x="179" y="30">
            <parameter key="number_of_validations" value="3"/>
            <parameter key="use_local_random_seed" value="true"/>
            <parameter key="local_random_seed" value="1994"/>
            <process expanded="true" height="399" width="280">
              <operator activated="true" class="logistic_regression" compatibility="5.1.001" expanded="true" height="94" name="Logistic Regression (2)" width="90" x="95" y="30">
                <parameter key="kernel_type" value="radial"/>
                <parameter key="kernel_gamma" value="2000.0"/>
                <parameter key="C" value="10.0"/>
              </operator>
              <connect from_port="training" to_op="Logistic Regression (2)" to_port="training set"/>
              <connect from_op="Logistic Regression (2)" from_port="model" to_port="model"/>
              <portSpacing port="source_training" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
              <portSpacing port="sink_through 1" spacing="0"/>
            </process>
            <process expanded="true" height="399" width="280">
              <operator activated="true" class="apply_model" compatibility="5.1.001" expanded="true" height="76" name="Apply Model (2)" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance_binominal_classification" compatibility="5.1.001" expanded="true" height="76" name="Performance (2)" width="90" x="162" y="30">
                <parameter key="main_criterion" value="AUC"/>
                <parameter key="accuracy" value="false"/>
                <parameter key="AUC" value="true"/>
              </operator>
              <connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
              <connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
              <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
              <connect from_op="Performance (2)" from_port="performance" to_port="averagable 1"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_averagable 1" spacing="0"/>
              <portSpacing port="sink_averagable 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="log" compatibility="5.1.001" expanded="true" height="76" name="Log" width="90" x="361" y="31">
            <list key="log">
              <parameter key="gamma" value="operator.Logistic Regression (2).parameter.kernel_gamma"/>
              <parameter key="C" value="operator.Logistic Regression (2).parameter.C"/>
            </list>
          </operator>
          <connect from_port="input 1" to_op="Validation (2)" to_port="training"/>
          <connect from_op="Validation (2)" from_port="averagable 1" to_op="Log" to_port="through 1"/>
          <connect from_op="Log" from_port="through 1" to_port="performance"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="store" compatibility="5.1.001" expanded="true" height="60" name="Store" width="90" x="454" y="112">
        <parameter key="repository_entry" value="valid.param"/>
      </operator>
      <connect from_op="Retrieve" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="performance" to_port="result 1"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="parameter" to_op="Store" to_port="input"/>
      <connect from_op="Store" from_port="through" to_port="result 2"/>
      <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>
Tagged:

Answers

  • steffen
    steffen New Altair Community Member
    Hello confuzio

    Here is my dummy process: 

    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.1.001">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="5.1.001" expanded="true" name="Process">
        <process expanded="true" height="449" width="882">
          <operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Retrieve" width="90" x="45" y="210">
            <parameter key="repository_entry" value="//Samples/data/Golf"/>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="5.1.001" expanded="true" height="76" name="Select_Numerical_Predictors" width="90" x="179" y="210">
            <parameter key="attribute_filter_type" value="value_type"/>
            <parameter key="value_type" value="numeric"/>
          </operator>
          <operator activated="true" breakpoints="after" class="multiply" compatibility="5.1.001" expanded="true" height="94" name="Multiply" width="90" x="313" y="210"/>
          <operator activated="true" class="logistic_regression" compatibility="5.1.001" expanded="true" height="94" name="Logistic Regression" width="90" x="514" y="210">
            <parameter key="kernel_type" value="radial"/>
          </operator>
          <operator activated="true" class="apply_model" compatibility="5.1.001" expanded="true" height="76" name="Apply Model" width="90" x="715" y="210">
            <list key="application_parameters"/>
          </operator>
          <connect from_op="Retrieve" from_port="output" to_op="Select_Numerical_Predictors" to_port="example set input"/>
          <connect from_op="Select_Numerical_Predictors" from_port="example set output" to_op="Multiply" to_port="input"/>
          <connect from_op="Multiply" from_port="output 1" to_op="Logistic Regression" to_port="training set"/>
          <connect from_op="Multiply" from_port="output 2" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Logistic Regression" from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_op="Apply Model" from_port="labelled data" 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>
    which creates confidences aka a rough prob output. What does your process look like ?

    In general it is recommended to post the used process whenever possible. Make it easy for us to help you ;)

    hope this was helpful,

    steffen
  • confuzio
    confuzio New Altair Community Member
    Ok, thanks for your process. I figured out how to save the confidences.
    Just one more question: "which creates confidences aka a rough prob output" -- do you simply mean by logistic regression estimated probabilities? I'm confused about the "rough".
  • steffen
    steffen New Altair Community Member
    the answer is: Yes, I mean the estimated probabilities.

    Sorry for the confusion

    greetings,

    steffen
  • confuzio
    confuzio New Altair Community Member
    Thanks a lot for your help!

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