Best learner selector?

isuarez

Hi looking what each operator is doing in the new rapidminer i found a operator that accept a few learners for a given data and give you back the performance of the best does anybody remember the name of the operator because i missed it and now i cannot find it.

Thanks.

land

Hi,
I would recommend using a Select Subprocess operator with a grid parameter optimization, that will return the index of the best subprocess. If you insert a learner into the sub processes, you might use the Select Subprocess operator instead of the learner inside a cross-validation. Just like this:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input>
      <location/>
    </input>
    <output>
      <location/>
      <location/>
      <location/>
    </output>
    <macros/>
  </context>
  <operator activated="true" class="process" expanded="true" name="Process">
    <process expanded="true" height="547" width="764">
      <operator activated="true" class="generate_data" expanded="true" height="60" name="Generate Data" width="90" x="45" y="30"/>
      <operator activated="true" class="optimize_parameters_grid" expanded="true" height="94" name="Optimize Parameters (Grid)" width="90" x="179" y="30">
        <list key="parameters"/>
        <process expanded="true" height="565" width="782">
          <operator activated="true" class="x_validation" expanded="true" height="112" name="Validation" width="90" x="179" y="30">
            <description>A cross-validation evaluating a decision tree model.</description>
            <process expanded="true" height="654" width="466">
              <operator activated="true" class="select_subprocess" expanded="true" height="76" name="Select Subprocess" width="90" x="45" y="30">
                <process expanded="true" height="547" width="357">
                  <operator activated="true" class="linear_regression" expanded="true" height="76" name="Linear Regression" width="90" x="72" y="30"/>
                  <connect from_port="input 1" to_op="Linear Regression" to_port="training set"/>
                  <connect from_op="Linear Regression" from_port="model" to_port="output 1"/>
                  <portSpacing port="source_input 1" spacing="0"/>
                  <portSpacing port="source_input 2" spacing="0"/>
                  <portSpacing port="sink_output 1" spacing="0"/>
                  <portSpacing port="sink_output 2" spacing="0"/>
                </process>
                <process expanded="true" height="547" width="357">
                  <operator activated="true" class="support_vector_machine" expanded="true" height="112" name="SVM" width="90" x="112" y="30"/>
                  <connect from_port="input 1" to_op="SVM" to_port="training set"/>
                  <connect from_op="SVM" from_port="model" to_port="output 1"/>
                  <portSpacing port="source_input 1" spacing="0"/>
                  <portSpacing port="source_input 2" spacing="0"/>
                  <portSpacing port="sink_output 1" spacing="0"/>
                  <portSpacing port="sink_output 2" spacing="0"/>
                </process>
              </operator>
              <connect from_port="training" to_op="Select Subprocess" to_port="input 1"/>
              <connect from_op="Select Subprocess" from_port="output 1" 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="654" width="466">
              <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
              <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"/>
              <connect from_op="Performance" 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>
          <connect from_port="input 1" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="averagable 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>
      <connect from_op="Generate Data" from_port="output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="performance" to_port="result 2"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="parameter" 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>

Greetings,
  Sebastian