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neural network performance measurement not possible
Learner I
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
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>
0
Answers