"R model error"

RLynxRLynx Member Posts: 18 Maven
edited May 2019 in Help
Why do I get this error? Thank you.

Nov 2, 2010 5:25:40 PM INFO: Saved process definition at '//Repository/57459910/57459910_experimentR 2'.
Nov 2, 2010 5:25:40 PM INFO: No filename given for result file, using stdout for logging results!
Nov 2, 2010 5:25:40 PM INFO: Loading initial data.
Nov 2, 2010 5:25:40 PM INFO: Process //Repository/57459910/57459910_experimentR 2 starts
Nov 2, 2010 5:25:40 PM WARNING: Using deprecated example set stream version 1
Nov 2, 2010 5:25:40 PM SEVERE: Naive Bayes: Warning in prep.data(TRUE, data, target, excluded, prep.ctrl) :
  Empty levels were dropped from class col.: Iris-virginica

Nov 2, 2010 5:25:41 PM SEVERE: Process failed: An error occurred while executing R.
Nov 2, 2010 5:25:41 PM SEVERE: Here:          Process[1] (Process)
          subprocess 'Main Process'
            +- Retrieve[1] (Retrieve)
            +- Validation[1] (Sliding Window Validation)
          subprocess 'Training'
                |  +- Decision Tree[0] (Decision Tree)
      ==>      |  +- Naive Bayes[1] (Naive Bayes)
          subprocess 'Testing'
                  +- Apply Model[0] (Apply Model)
                  +- Performance[0] (Performance)


<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <operator activated="true" class="process" compatibility="5.0.11" expanded="true" name="Process">
    <process expanded="true" height="522" width="549">
      <operator activated="true" class="retrieve" compatibility="5.0.11" expanded="true" height="60" name="Retrieve" width="90" x="155" y="205">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      <operator activated="true" class="series:sliding_window_validation" compatibility="5.0.2" expanded="true" height="112" name="Validation" width="90" x="429" y="216">
        <parameter key="training_window_width" value="90"/>
        <parameter key="test_window_width" value="1"/>
        <parameter key="horizon" value="10"/>
        <parameter key="average_performances_only" value="false"/>
        <process expanded="true" height="522" width="346">
          <operator activated="false" class="decision_tree" compatibility="5.0.11" expanded="true" height="76" name="Decision Tree" width="90" x="179" y="255">
            <parameter key="criterion" value="gini_index"/>
          <operator activated="true" class="r:naive_bayes" compatibility="5.0.2" expanded="true" height="76" name="Naive Bayes" width="90" x="154" y="46"/>
          <connect from_port="training" to_op="Naive Bayes" to_port="training set"/>
          <connect from_op="Naive Bayes" 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 expanded="true" height="522" width="300">
          <operator activated="true" class="apply_model" compatibility="5.0.11" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
            <list key="application_parameters"/>
          <operator activated="true" class="performance" compatibility="5.0.11" expanded="true" height="76" name="Performance" width="90" x="180" 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"/>
      <connect from_op="Retrieve" from_port="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"/>


  • RLynxRLynx Member Posts: 18 Maven
    Any ideas? I got stuck here.. Thank you in advance, again.
  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    your actual problem does not derive from the R-NaiveBayes but instead from using the SlidingWindow Validation that is made for time series, while you don't have a time series.
    Just exchange it by a normal cross-validation and everything should run fine.

  • RLynxRLynx Member Posts: 18 Maven
    Thank you for your reply. But.. original data that I use for my experiments (and it's not Iris data) requires Sliding Window Validation. Iris data here is just an example to show what kind of error I get with my original data.
    All non-R classification operators work perfectly fine with Sliding Windows Validation and Iris data. Check Decision Tree for example. Problem here is only with R operators.     
  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    ok, then unfortunately you will have to a problem until we solve it in the extension. You can use the NaiveBayes operator instead, this should work...

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