[SOLVED] the learner model does not support parameter

l__chine

Hello,
I've built a new process with data loaded from excel file and two learner models - naive bayers and SVM. When I tried a validation I've got a message: The learner model does not support a parameter. In my results perspective, the performance show zeros in all places. Can anyone explain me what does it mean?
Thank you

Nils_Woehler

Hi,

please post a process setup like it is described here: http://rapid-i.com/rapidforum/index.php/topic,4654.0.html

Best,
Nils

l__chine

Of course here there is:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.2.000" expanded="true" name="Process">
    <process expanded="true" height="626" width="1016">
      <operator activated="true" class="retrieve" compatibility="5.2.000" expanded="true" height="60" name="Retrieve" width="90" x="45" y="120">
        <parameter key="repository_entry" value="datasetfordatamining"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.2.000" expanded="true" height="76" name="Set Role" width="90" x="246" y="75">
        <parameter key="name" value="label"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation" width="90" x="380" y="30">
        <description>A cross-validation evaluating a linear regression model.</description>
        <parameter key="leave_one_out" value="true"/>
        <process expanded="true" height="654" width="480">
          <operator activated="true" class="multiply" compatibility="5.2.000" expanded="true" height="94" name="Multiply" width="90" x="49" y="43"/>
          <operator activated="true" class="naive_bayes_kernel" compatibility="5.2.000" expanded="true" height="76" name="Naive Bayes (Kernel)" width="90" x="112" y="165"/>
          <operator activated="true" class="support_vector_machine" compatibility="5.2.000" expanded="true" height="112" name="SVM" width="90" x="188" y="39">
            <parameter key="kernel_cache" value="300"/>
            <parameter key="C" value="1.0"/>
            <parameter key="max_iterations" value="10000"/>
            <parameter key="balance_cost" value="true"/>
            <parameter key="quadratic_loss_pos" value="true"/>
            <parameter key="quadratic_loss_neg" value="true"/>
          </operator>
          <operator activated="true" class="group_models" compatibility="5.2.000" expanded="true" height="94" name="Group Models (2)" width="90" x="313" y="120"/>
          <connect from_port="training" to_op="Multiply" to_port="input"/>
          <connect from_op="Multiply" from_port="output 1" to_op="SVM" to_port="training set"/>
          <connect from_op="Multiply" from_port="output 2" to_op="Naive Bayes (Kernel)" to_port="training set"/>
          <connect from_op="Naive Bayes (Kernel)" from_port="model" to_op="Group Models (2)" to_port="models in 2"/>
          <connect from_op="SVM" from_port="model" to_op="Group Models (2)" to_port="models in 1"/>
          <connect from_op="Group Models (2)" from_port="model out" to_port="model"/>
          <portSpacing port="source_training" spacing="18"/>
          <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" compatibility="5.2.000" expanded="true" height="76" name="Apply Model" width="90" x="112" y="75">
            <list key="application_parameters"/>
            <parameter key="create_view" value="true"/>
          </operator>
          <operator activated="true" class="write_model" compatibility="5.2.000" expanded="true" height="60" name="Write Model" width="90" x="246" y="255">
            <parameter key="model_file" value="C:\Users\Lenka\Documents\RapidMinerRepository\modelfile"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.2.000" expanded="true" height="76" name="Performance" width="90" x="246" y="75"/>
          <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="Apply Model" from_port="model" to_op="Write Model" to_port="input"/>
          <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
          <portSpacing port="source_model" spacing="36"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="90"/>
          <portSpacing port="sink_averagable 1" spacing="0"/>
          <portSpacing port="sink_averagable 2" spacing="0"/>
        </process>
      </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="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="model" to_port="result 2"/>
      <connect from_op="Validation" from_port="training" to_port="result 3"/>
      <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"/>
      <portSpacing port="sink_result 3" spacing="0"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

my exaple set conected with learners contains a set of attributes(i.e. age, height, weight...) of different data types (some of them int some of them real or binominal). The first attribute is a label and has two different name(i.e. adult and child). After running process and switching in a results table, performancevector display this two names but shows zero of evaluated examples. In the info box I see a message: Kernel Model: The learned model does not support a parameter, Kernel Distribution:The learned model does not support a parameter. I predict there is a relationship between the message in info box and evaluating my data.

Nils_Woehler

Hi,

fist of all what are you trying to achieve with your process? The Group Models Operator groups your Classifiers but applies them sequentially afterwards .
Thus only the last classifier will determine the prediction result.

Best,
Nils

l__chine

Ok. I always give to process one measurement which has different attributies but unknown label name.
I try to achieve a prediction label which displays what label name, based on label names in given example set, the learner recognize. I finaly succeed when I had changed an estimation mode in Naive Bayers operator from greedy to full. Everything is going fine now.