"Regarding training Performance metrics in cross Validation"

varunm1

Hi,

I am looking to get performance metrics (AUC, Accuracy & RMSE) during training in a cross-validation operator. Are there any suggestions for this?

@lionelderkrikor @Telcontar120

Thanks
Varun

lionelderkrikor

Hi @varunm1,

 To have a general idea of the training error is to connect your training set (tra) to the Apply Model operator via the thr port (through port).
Take a look at this process : 

<?xml version="1.0" encoding="UTF-8"?><process version="9.1.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.1.000" 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="9.1.000" expanded="true" height="68" name="Retrieve Golf" width="90" x="112" y="85">
        <parameter key="repository_entry" value="//Samples/data/Golf"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.1.000" expanded="true" height="82" name="Set Role" width="90" x="246" y="85">
        <parameter key="attribute_name" value="Play"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="9.1.000" expanded="true" height="145" name="Cross Validation" width="90" x="447" y="85">
        <parameter key="split_on_batch_attribute" value="false"/>
        <parameter key="leave_one_out" value="false"/>
        <parameter key="number_of_folds" value="10"/>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="enable_parallel_execution" value="true"/>
        <process expanded="true">
          <operator activated="true" class="multiply" compatibility="9.1.000" expanded="true" height="103" name="Multiply" width="90" x="179" y="34"/>
          <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.1.000" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="187">
            <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>
          <connect from_port="training set" to_op="Multiply" to_port="input"/>
          <connect from_op="Multiply" from_port="output 1" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Multiply" from_port="output 2" to_port="through 1"/>
          <connect from_op="Decision Tree" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
          <portSpacing port="sink_through 2" spacing="0"/>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="9.1.000" expanded="true" height="82" name="Apply Model" width="90" x="112" y="34">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="performance_classification" compatibility="9.1.000" expanded="true" height="82" name="Performance" width="90" x="246" y="34">
            <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_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="through 1" 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="performance 1"/>
          <connect from_op="Performance" from_port="example set" to_port="test set results"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="source_through 2" spacing="0"/>
          <portSpacing port="sink_test set results" spacing="0"/>
          <portSpacing port="sink_performance 1" spacing="0"/>
          <portSpacing port="sink_performance 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Golf" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Cross Validation" to_port="example set"/>
      <connect from_op="Cross Validation" from_port="test result set" to_port="result 2"/>
      <connect from_op="Cross Validation" from_port="performance 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"/>
    </process>
  </operator>
</process>

I hope it helps,

Regards and happy holidays,

Lionel

hughesfleming68

Hi Varun, have you tried logging the output using the log operator? Also see if connecting the performance to data operator inside the cross validation operator does what you want.

Telcontar120

As my colleagues have mentioned, it is possible to get this information from RapidMiner using the Log operator.  However, I would be quite careful, typically the training error is NOT useful for understanding your model performance.  That's the whole reason you are doing cross-validation, to understand the error on the test set instead.

varunm1

Hi @Telcontar120. Thanks for your response. I am looking into CV test performance, this training performance is to compare with some other work going on.

@hughesfleming68 @lionelderkrikor Thank you.

IngoRM

Just wanted to chime in since this is a topic I care a lot about (as many probably know by now )  Even this type of comparison can be pretty much useless.  Reminder: the most simple machine learning model in the world (K-Nearest Neighbors with k=1) has always training error of 0%

Anyway, here is my "magnum opus" on validations and why training errors should be always completely ignored IMHO:

https://rapidminer.com/resource/correct-model-validation/

Hope I do not sound like a cranky school teacher here though...

Cheers,
Ingo