Manual inspection of missclassified examples

Carl_Granström

Hello,

I'm trying to find out how, after training a classification model, I can look at the examples that were incorrectly classified. For now I can only see how many examples were incorrectly classified in the confusion matrix, but I want to inspect the missclassified examples manually. Since evaluation vector does not seem to be able to store such information I guess I need to somehow add another operator to achieve this, if it's even possible (which, in my own opinion, feels like a very basic feature, so I'm hoping it's there somewhere).

Kind regards,

Carl

sgenzer

hi @Carl_Granström hmm well that does sound very basic. Funny thing is that I moderate this forum and have been on it for years - I cannot recall anyone asking!

Anyway it's pretty easy. I would just put a Filter Examples on the end like this:

<?xml version="1.0" encoding="UTF-8"?><process version="9.5.000-BETA4">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.5.000-BETA4" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="-1"/>
    <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="9.5.000-BETA4" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Titanic Training"/>
      </operator>
      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.5.000-BETA4" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="34">
        <parameter key="criterion" value="gain_ratio"/>
        <parameter key="maximal_depth" value="10"/>
        <parameter key="apply_pruning" value="true"/>
        <parameter key="confidence" value="0.1"/>
        <parameter key="apply_prepruning" value="true"/>
        <parameter key="minimal_gain" value="0.01"/>
        <parameter key="minimal_leaf_size" value="2"/>
        <parameter key="minimal_size_for_split" value="4"/>
        <parameter key="number_of_prepruning_alternatives" value="3"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.5.000-BETA4" expanded="true" height="82" name="Apply Model" width="90" x="380" y="34">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="9.5.000-BETA4" expanded="true" height="103" name="Filter Examples" width="90" x="514" y="34">
        <parameter key="parameter_expression" value="Survived!=[prediction(Survived)]"/>
        <parameter key="condition_class" value="expression"/>
        <parameter key="invert_filter" value="false"/>
        <list key="filters_list"/>
        <parameter key="filters_logic_and" value="true"/>
        <parameter key="filters_check_metadata" value="true"/>
        <description align="center" color="yellow" colored="true" width="126">here's where I only find incorrect predictions</description>
      </operator>
      <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Decision Tree" to_port="training set"/>
      <connect from_op="Decision Tree" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Decision Tree" from_port="exampleSet" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Apply Model" from_port="labelled data" to_op="Filter Examples" to_port="example set input"/>
      <connect from_op="Filter Examples" from_port="example set output" 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>

Scott