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neural network performance measurement not possible

TB161TB161 Member Posts: 5 Newbie
Hello Community Members,
I have a question about neural network performance measurement. I would like to measure the performance as described in the textbooks, for this it is described that the values ​​must be discrete or nominal. This does not work with neural networks. What am I doing wrong would be great if you could help me. The model is attached.
Regards TB




<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="retrieve" compatibility="9.7.001" expanded="true" height="68" name="Retrieve Datapreperation_V10 (9)" width="90" x="45" y="34">
    <parameter key="repository_entry" value="//Shared IntroDS/Data for Processes/Datapreperation_V10"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="set_role" compatibility="9.7.001" expanded="true" height="82" name="Set Role (10)" width="90" x="179" y="34">
    <parameter key="attribute_name" value="Kaufpreis"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="select_attributes" compatibility="9.7.001" expanded="true" height="82" name="Select Attributes (8)" width="90" x="313" y="34">
    <parameter key="attribute_filter_type" value="subset"/>
    <parameter key="attribute" value=""/>
    <parameter key="attributes" value="Kaufpreis|Wohnfläche|Zimmeranzahl|Baujahr|Bundesland|Ort"/>
    <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"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="nominal_to_numerical" compatibility="9.7.001" expanded="true" height="103" name="Nominal to Numerical (5)" width="90" x="447" y="34">
    <parameter key="return_preprocessing_model" value="false"/>
    <parameter key="create_view" value="false"/>
    <parameter key="attribute_filter_type" value="all"/>
    <parameter key="attribute" value=""/>
    <parameter key="attributes" value=""/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="nominal"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="file_path"/>
    <parameter key="block_type" value="single_value"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="single_value"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="false"/>
    <parameter key="coding_type" value="dummy coding"/>
    <parameter key="use_comparison_groups" value="false"/>
    <list key="comparison_groups"/>
    <parameter key="unexpected_value_handling" value="all 0 and warning"/>
    <parameter key="use_underscore_in_name" value="false"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="split_data" compatibility="9.7.001" expanded="true" height="103" name="Split Data" width="90" x="581" y="34">
    <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>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="neural_net" compatibility="9.7.001" expanded="true" height="82" name="Neural Net (2)" width="90" x="715" y="34">
    <list key="hidden_layers"/>
    <parameter key="training_cycles" value="50"/>
    <parameter key="learning_rate" value="0.01"/>
    <parameter key="momentum" value="0.9"/>
    <parameter key="decay" value="false"/>
    <parameter key="shuffle" value="true"/>
    <parameter key="normalize" value="true"/>
    <parameter key="error_epsilon" value="1.0E-4"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="apply_model" compatibility="9.7.001" expanded="true" height="82" name="Apply Model (4)" width="90" x="782" y="187">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="discretize_by_frequency" compatibility="9.7.001" expanded="true" height="103" name="Discretize (4)" width="90" x="916" y="187">
    <parameter key="return_preprocessing_model" value="false"/>
    <parameter key="create_view" value="false"/>
    <parameter key="attribute_filter_type" value="all"/>
    <parameter key="attribute" value=""/>
    <parameter key="attributes" value=""/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="numeric"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="real"/>
    <parameter key="block_type" value="value_series"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="value_series_end"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="false"/>
    <parameter key="use_sqrt_of_examples" value="false"/>
    <parameter key="number_of_bins" value="3"/>
    <parameter key="range_name_type" value="long"/>
    <parameter key="automatic_number_of_digits" value="true"/>
    <parameter key="number_of_digits" value="-1"/>
  </operator>
</process>
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
  <operator activated="true" class="performance_classification" compatibility="9.7.001" expanded="true" height="82" name="Performance (2)" width="90" x="1050" y="187">
    <parameter key="main_criterion" value="first"/>
    <parameter key="accuracy" value="true"/>
    <parameter key="classification_error" value="true"/>
    <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>
</process>



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