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[HELP NEEED] Perfomance Classfication Error

tenztenz Member Posts: 3 Newbie
edited March 12 in Help
I want to see my performance model, my prediction is multi-classification type (0,1,2) but there is an error when it applies performance(Classification) operators "Performance(classification) cannot handle label." 
Then I try to use the recommendation help solver. There is another error "label and prediction must be the same type but are nominal and integer, respectively. " then I try to change the performance(regression), and it works, but that is not what I want. Please help me to solve this thing, thank you.

Result:  

Data type:



The first error using performance(classification):


The second error using performance(classification) with help me solve the problem:

Xml Code
<?xml version="1.0" encoding="UTF-8"?><process version="9.8.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.8.001" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.8.001" expanded="true" height="68" name="Retrieve train" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Local Repository/train"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.8.001" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">
        <parameter key="attribute_name" value="price_range"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="split_data" compatibility="9.8.001" expanded="true" height="103" name="Split Data" width="90" x="447" y="238">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.7"/>
          <parameter key="ratio" value="0.3"/>
        </enumeration>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="k_nn" compatibility="9.8.001" expanded="true" height="82" name="k-NN" width="90" x="581" y="34">
        <parameter key="k" value="5"/>
        <parameter key="weighted_vote" value="true"/>
        <parameter key="measure_types" value="MixedMeasures"/>
        <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
        <parameter key="nominal_measure" value="NominalDistance"/>
        <parameter key="numerical_measure" value="EuclideanDistance"/>
        <parameter key="divergence" value="GeneralizedIDivergence"/>
        <parameter key="kernel_type" value="radial"/>
        <parameter key="kernel_gamma" value="1.0"/>
        <parameter key="kernel_sigma1" value="1.0"/>
        <parameter key="kernel_sigma2" value="0.0"/>
        <parameter key="kernel_sigma3" value="2.0"/>
        <parameter key="kernel_degree" value="3.0"/>
        <parameter key="kernel_shift" value="1.0"/>
        <parameter key="kernel_a" value="1.0"/>
        <parameter key="kernel_b" value="0.0"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.8.001" expanded="true" height="82" name="Apply Model" width="90" x="715" y="136">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="performance_classification" compatibility="9.8.001" expanded="true" height="82" name="Performance" width="90" x="849" y="289">
        <parameter key="main_criterion" value="first"/>
        <parameter key="accuracy" value="true"/>
        <parameter key="classification_error" value="false"/>
        <parameter key="kappa" value="false"/>
        <parameter key="weighted_mean_recall" value="false"/>
        <parameter key="weighted_mean_precision" value="false"/>
        <parameter key="spearman_rho" value="false"/>
        <parameter key="kendall_tau" value="false"/>
        <parameter key="absolute_error" value="false"/>
        <parameter key="relative_error" value="false"/>
        <parameter key="relative_error_lenient" value="false"/>
        <parameter key="relative_error_strict" value="false"/>
        <parameter key="normalized_absolute_error" value="false"/>
        <parameter key="root_mean_squared_error" value="false"/>
        <parameter key="root_relative_squared_error" value="false"/>
        <parameter key="squared_error" value="false"/>
        <parameter key="correlation" value="false"/>
        <parameter key="squared_correlation" value="false"/>
        <parameter key="cross-entropy" value="false"/>
        <parameter key="margin" value="false"/>
        <parameter key="soft_margin_loss" value="false"/>
        <parameter key="logistic_loss" value="false"/>
        <parameter key="skip_undefined_labels" value="true"/>
        <parameter key="use_example_weights" value="true"/>
        <list key="class_weights"/>
      </operator>
      <connect from_op="Retrieve train" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Split Data" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 1" to_op="k-NN" to_port="training set"/>
      <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="k-NN" from_port="model" to_op="Apply Model" to_port="model"/>
      <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_port="result 1"/>
      <connect from_op="Performance" from_port="performance" to_port="result 2"/>
      <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>


Answers

  • ceaperezceaperez Member Posts: 195   Unicorn
    Hi @tenz

    You are trying to run a multi-classification model with a numerical label. please change the  price_range from numerical to nominal. 
    As example you can run your xlm model with the iris dataset from examples. 



    regards
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