NeuralNet Polynomial not supported

yanika1716yanika1716 Member Posts: 3 Contributor I
edited November 2018 in Help
Hi there !

I have a dataset (excel format) on which I want to run the neural net algorithm but I am getting this error mesage;

"The operator NeuralNet does not have sufficient capabilities for the given data set; polynomial attributes not supported"

Can anyone help me with this?

Thanks!

Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,249 RM Data Scientist
    Hello yanika,

    The Neural Net operator can only work on numerical values. So usually you convert the polynominal values to numerical values just including 1's and 0's

    Before you had something like

    MyCategory
    Cat1
    Cat2

    Afterwards you have

    MyCategory=Cat1 MyCategory=Cat2
    1                            0
    0                            1

    The converting operator is Nominal to Numerical. Be careful using it. You might get hundrets of attributes

    Here is an example process:

    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="6.2.000">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="6.2.000" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="6.2.000" expanded="true" height="60" name="Retrieve Golf" width="90" x="45" y="75">
            <parameter key="repository_entry" value="//Samples/data/Golf"/>
          </operator>
          <operator activated="true" class="nominal_to_numerical" compatibility="6.2.000" expanded="true" height="94" name="Nominal to Numerical" width="90" x="179" y="75">
            <list key="comparison_groups"/>
          </operator>
          <operator activated="true" class="x_validation" compatibility="5.0.000" expanded="true" height="112" name="Validation" width="90" x="313" y="75">
            <description>A cross-validation evaluating a decision tree model.</description>
            <process expanded="true">
              <operator activated="true" class="neural_net" compatibility="6.2.000" expanded="true" height="76" name="Neural Net" width="90" x="112" y="120">
                <list key="hidden_layers"/>
              </operator>
              <connect from_port="training" to_op="Neural Net" to_port="training set"/>
              <connect from_op="Neural Net" 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">
              <operator activated="true" class="apply_model" compatibility="5.0.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" compatibility="5.0.000" 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_op="Retrieve Golf" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
          <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="averagable 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>
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • yanika1716yanika1716 Member Posts: 3 Contributor I
    should I insert an operator between the data set and the neural net algorithm? I am totally new to RapidMiner... this is my first time using it  :-\
  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,249 RM Data Scientist
    yes you should

    have you seend our additional ressources? http://docs.rapidminer.com/resources/ The book "Data Mining for the Masses" might be good for you.
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
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