Currently I am predicting creditor-defaults, using (besides others) Kernel Logistic Regression. Here's my problem: KLR predicts probabilities ("confidences") which are quite a bit higher than those given by a quite accurate Generalized Additive Model; leading to a much worse average performance (% deviance explained).
Comparing the Logits of the predicted probabilities it could be that there is just a constant missing, but I'm not sure if that is where the problem comes from.
Below my simple (not) working example. I really appreciate your help!
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.001" expanded="true" name="Process">
<process expanded="true" height="415" width="882">
<operator activated="true" class="read_csv" compatibility="5.1.001" expanded="true" height="60" name="Read CSV" width="90" x="45" y="120">
<parameter key="csv_file" value="D:\DiplomModellierung\workspace\VSP35.csv"/>
<parameter key="column_separators" value=","/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations">
<parameter key="0" value="Name"/>
</list>
<list key="data_set_meta_data_information">
<parameter key="0" value=".false.integer.attribute"/>
<parameter key="1" value="default.true.binominal.label"/>
<parameter key="2" value="score.true.real.attribute"/>
<parameter key="3" value="d\.score.true.real.attribute"/>
<parameter key="4" value="d2\.score.true.real.attribute"/>
<parameter key="5" value="NoND.true.real.attribute"/>
<parameter key="6" value="WVP.true.real.attribute"/>
</list>
</operator>
<operator activated="true" class="logistic_regression" compatibility="5.1.001" expanded="true" height="94" name="Logistic Regression" width="90" x="246" y="120">
<parameter key="kernel_gamma" value="0.52"/>
</operator>
<operator activated="true" class="read_csv" compatibility="5.1.001" expanded="true" height="60" name="Read CSV (2)" width="90" x="246" y="255">
<parameter key="csv_file" value="D:\DiplomModellierung\workspace\TSP35.csv"/>
<parameter key="column_separators" value=","/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations">
<parameter key="0" value="Name"/>
</list>
<list key="data_set_meta_data_information">
<parameter key="0" value=".false.integer.attribute"/>
<parameter key="1" value="default.true.binominal.label"/>
<parameter key="2" value="score.true.real.attribute"/>
<parameter key="3" value="d\.score.true.real.attribute"/>
<parameter key="4" value="d2\.score.true.real.attribute"/>
<parameter key="5" value="NoND.true.real.attribute"/>
<parameter key="6" value="WVP.true.real.attribute"/>
</list>
</operator>
<operator activated="true" class="apply_model" compatibility="5.1.001" expanded="true" height="76" name="Apply Model" width="90" x="380" y="165">
<list key="application_parameters"/>
<parameter key="create_view" value="true"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="5.1.001" expanded="true" height="76" name="Performance" width="90" x="514" y="165">
<parameter key="accuracy" value="false"/>
<parameter key="cross-entropy" value="true"/>
<list key="class_weights"/>
</operator>
<operator activated="true" class="write_csv" compatibility="5.1.001" expanded="true" height="60" name="Write CSV" width="90" x="648" y="210">
<parameter key="csv_file" value="C:\Users\Richard\Desktop\test.csv"/>
</operator>
<operator activated="true" class="log" compatibility="5.1.001" expanded="true" height="76" name="Log" width="90" x="648" y="75">
<parameter key="filename" value="C:\Users\Richard\Desktop\loganwendung40.log"/>
<list key="log">
<parameter key="crossentropy" value="operator.Performance.value.cross-entropy"/>
</list>
<parameter key="persistent" value="true"/>
</operator>
<connect from_op="Read CSV" from_port="output" to_op="Logistic Regression" to_port="training set"/>
<connect from_op="Logistic Regression" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Read CSV (2)" from_port="output" 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_op="Log" to_port="through 1"/>
<connect from_op="Performance" from_port="example set" to_op="Write CSV" to_port="input"/>
<connect from_op="Log" from_port="through 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"/>
</process>
</operator>
</process>
