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Calculations for the Pos and Neg Predictive Values and the PSEP in binomial performance

User: "kdafoe"
kdafoe
New Altair Community Member
Updated by Jocelyn
Hi. I am trying to understand a few calculations in the Binomial Performance Classification operator. I am using the Titanic dataset, and a decision tree operator within the nominal cross-validation building block. I switched the performance operator for the binomial classification performance operator so that I could get more criterion. Everything looks great (meaning I can verify the values provided), except for three values.

The value Rapidminer shows for the Positive Predictive Value is 82.87%, which matches my calculation for a Negative Predictive Value. The reverse is also true that the Negative Predictive Value of 75.49% matches my Positive value. Is there a labeling mis-match? There are more negative (no) survival values in the Titanic dataset than positive (yes) values, so I think my values are correct.

Also, how do you calculate the PSEP or Positive Satisfactory Error Probability value of .584? The equation I use is FPR + FNR * (1- Acceptable Error Rate), but after substituting FPR and FNR values the only value that matches your score for the Acceptable Error Rate is -0.532. But it doesn't make sense that an Acceptable Error Rate is a negative value, nor do I understand how you arrive at 53.2%? 

Can someone help explain these differences to me?

Thanks for you time.



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    User: "kdafoe"
    kdafoe
    New Altair Community Member
    OP
    Here, is the XML code

    ?xml version="1.0" encoding="UTF-8"?><process version="10.1.002">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="10.1.002" 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="10.1.002" expanded="true" height="68" name="Retrieve Titanic" width="90" x="179" y="187">
            <parameter key="repository_entry" value="//Samples/data/Titanic"/>
          </operator>
          <operator activated="true" class="blending:select_attributes" compatibility="10.1.002" expanded="true" height="82" name="Select Attributes" width="90" x="313" y="187">
            <parameter key="type" value="exclude attributes"/>
            <parameter key="attribute_filter_type" value="a subset"/>
            <parameter key="select_attribute" value=""/>
            <parameter key="select_subset" value="Cabin␞Life Boat␞Name␞Port of Embarkation␞Ticket Number"/>
            <parameter key="also_apply_to_special_attributes_(id,_label..)" value="false"/>
          </operator>
          <operator activated="true" class="blending:set_role" compatibility="10.1.002" expanded="true" height="82" name="Set Role" width="90" x="447" y="187">
            <list key="set_roles">
              <parameter key="Survived" value="label"/>
            </list>
          </operator>
          <operator activated="true" class="impute_missing_values" compatibility="10.1.002" expanded="true" height="68" name="Impute Missing Values" width="90" x="581" y="187">
            <parameter key="attribute_filter_type" value="all"/>
            <parameter key="attribute" value="Age"/>
            <parameter key="attributes" value=""/>
            <parameter key="use_except_expression" value="false"/>
            <parameter key="value_type" value="attribute_value"/>
            <parameter key="use_value_type_exception" value="false"/>
            <parameter key="except_value_type" value="time"/>
            <parameter key="block_type" value="attribute_block"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="value_matrix_row_start"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="false"/>
            <parameter key="iterate" value="true"/>
            <parameter key="learn_on_complete_cases" value="true"/>
            <parameter key="order" value="chronological"/>
            <parameter key="sort" value="ascending"/>
            <parameter key="use_local_random_seed" value="false"/>
            <parameter key="local_random_seed" value="1992"/>
            <process expanded="true">
              <operator activated="true" class="k_nn" compatibility="10.1.002" expanded="true" height="82" name="k-NN" width="90" x="112" y="136">
                <parameter key="k" value="2"/>
                <parameter key="weighted_vote" value="false"/>
                <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>
              <connect from_port="example set source" to_op="k-NN" to_port="training set"/>
              <connect from_op="k-NN" from_port="model" to_port="model sink"/>
              <portSpacing port="source_example set source" spacing="0"/>
              <portSpacing port="sink_model sink" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="concurrency:cross_validation" compatibility="10.0.000" expanded="true" height="145" name="Validation" width="90" x="782" y="238">
            <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="stratified sampling"/>
            <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="concurrency:parallel_decision_tree" compatibility="10.1.002" expanded="true" height="103" name="Decision Tree (2)" width="90" x="45" 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>
              <connect from_port="training set" to_op="Decision Tree (2)" to_port="training set"/>
              <connect from_op="Decision Tree (2)" 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"/>
              <description align="left" color="green" colored="true" height="80" resized="true" width="248" x="37" y="158">In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)</description>
            </process>
            <process expanded="true">
              <operator activated="true" class="apply_model" compatibility="10.1.002" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
                <list key="application_parameters"/>
              </operator>
              <operator activated="false" class="performance" compatibility="10.1.002" expanded="true" height="82" name="Performance" width="90" x="112" y="340">
                <parameter key="use_example_weights" value="true"/>
              </operator>
              <operator activated="true" class="performance_binominal_classification" compatibility="10.1.002" expanded="true" height="82" name="Performance (2)" width="90" x="179" y="34">
                <parameter key="manually_set_positive_class" value="false"/>
                <parameter key="main_criterion" value="first"/>
                <parameter key="accuracy" value="true"/>
                <parameter key="classification_error" value="true"/>
                <parameter key="kappa" value="true"/>
                <parameter key="AUC (optimistic)" value="false"/>
                <parameter key="AUC" value="true"/>
                <parameter key="AUC (pessimistic)" value="false"/>
                <parameter key="precision" value="true"/>
                <parameter key="recall" value="true"/>
                <parameter key="lift" value="false"/>
                <parameter key="fallout" value="false"/>
                <parameter key="f_measure" value="true"/>
                <parameter key="false_positive" value="true"/>
                <parameter key="false_negative" value="true"/>
                <parameter key="true_positive" value="true"/>
                <parameter key="true_negative" value="true"/>
                <parameter key="sensitivity" value="true"/>
                <parameter key="specificity" value="true"/>
                <parameter key="youden" value="true"/>
                <parameter key="positive_predictive_value" value="true"/>
                <parameter key="negative_predictive_value" value="true"/>
                <parameter key="psep" value="true"/>
                <parameter key="skip_undefined_labels" value="true"/>
                <parameter key="use_example_weights" value="true"/>
              </operator>
              <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 (2)" to_port="labelled data"/>
              <connect from_op="Performance (2)" from_port="performance" to_port="performance 1"/>
              <connect from_op="Performance (2)" 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="sink_test set results" spacing="0"/>
              <portSpacing port="sink_performance 1" spacing="0"/>
              <portSpacing port="sink_performance 2" spacing="0"/>
              <description align="left" color="blue" colored="true" height="103" resized="true" width="315" x="38" y="158">The model created in the Training step is applied to the current test set (10 %).&lt;br/&gt;The performance is evaluated and sent to the operator results.</description>
            </process>
            <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
          </operator>
          <operator activated="true" class="model_simulator:model_simulator" compatibility="10.1.000" expanded="true" height="103" name="Model Simulator" width="90" x="983" y="391"/>
          <connect from_op="Retrieve Titanic" from_port="output" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>
          <connect from_op="Set Role" from_port="example set output" to_op="Impute Missing Values" to_port="example set in"/>
          <connect from_op="Impute Missing Values" from_port="example set out" to_op="Validation" to_port="example set"/>
          <connect from_op="Validation" from_port="model" to_op="Model Simulator" to_port="model"/>
          <connect from_op="Validation" from_port="example set" to_op="Model Simulator" to_port="training data"/>
          <connect from_op="Validation" from_port="test result set" to_op="Model Simulator" to_port="test data"/>
          <connect from_op="Validation" from_port="performance 1" to_port="result 3"/>
          <connect from_op="Model Simulator" from_port="simulator output" to_port="result 1"/>
          <connect from_op="Model Simulator" from_port="model output" 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"/>
          <portSpacing port="sink_result 4" spacing="0"/>
          <description align="center" color="yellow" colored="false" height="81" resized="true" width="422" x="503" y="33">Simple Titanic Decision Tree Model</description>
        </process>
      </operator>
    </process>
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