using AutoMLP operator

Best Answer

• Options

  Thomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761 Unicorn

  July 2017 Solution Accepted

  You use it like any other learner in RapidMiner, see example below:

   

  <?xml version="1.0" encoding="UTF-8"?><process version="7.5.003">
    <context>
      <input/>
      <output/>
      <macros/>
    </context>
    <operator activated="true" class="process" compatibility="7.5.003" expanded="true" name="Process">
      <process expanded="true">
        <operator activated="true" class="retrieve" compatibility="7.5.003" expanded="true" height="68" name="Retrieve Iris" width="90" x="45" y="34">
          <parameter key="repository_entry" value="//Samples/data/Iris"/>
        </operator>
        <operator activated="true" class="concurrency:cross_validation" compatibility="7.5.003" expanded="true" height="145" name="Validation" width="90" x="179" y="34">
          <parameter key="sampling_type" value="stratified sampling"/>
          <process expanded="true">
            <operator activated="true" class="auto_mlp" compatibility="7.5.003" expanded="true" height="82" name="AutoMLP" width="90" x="179" y="34"/>
            <connect from_port="training set" to_op="AutoMLP" to_port="training set"/>
            <connect from_op="AutoMLP" from_port="model" to_port="model"/>
            <portSpacing port="source_training set" spacing="0"/>
            <portSpacing port="sink_model" spacing="0"/>
            <portSpacing port="sink_through 1" spacing="0"/>
            <description align="left" color="green" colored="true" height="80" resized="true" width="248" x="37" y="137">In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)</description>
          </process>
          <process expanded="true">
            <operator activated="true" class="apply_model" compatibility="7.5.003" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
              <list key="application_parameters"/>
            </operator>
            <operator activated="true" class="performance" compatibility="7.5.003" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/>
            <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="performance 1"/>
            <connect from_op="Performance" from_port="example set" to_port="test set results"/>
            <portSpacing port="source_model" spacing="0"/>
            <portSpacing port="source_test set" spacing="0"/>
            <portSpacing port="source_through 1" spacing="0"/>
            <portSpacing port="sink_test set results" spacing="0"/>
            <portSpacing port="sink_performance 1" spacing="0"/>
            <portSpacing port="sink_performance 2" spacing="0"/>
            <description align="left" color="blue" colored="true" height="103" resized="true" width="315" x="38" y="137">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>
          </process>
          <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
        </operator>
        <connect from_op="Retrieve Iris" from_port="output" to_op="Validation" to_port="example set"/>
        <connect from_op="Validation" from_port="performance 1" 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>
  

  1