Hi,
I have been wasting a while on the SVM optimization (or maybe not). The question is more or less same as
http://rapid-i.com/rapidforum/index.php/topic,1573.0.html, From Rapido ,
The idea is
1. generate data (binomial label)
2. transform the data
3. Parameter optimization with xvalidation, but AUC will be the main control parameter
4. Draw the best parameter's ROC and show AUC
but when i ran the process, the output is always negtive. (the machine judge everything to negtive), This really confused me... Thanks for advance
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<description />
<parameter key="logverbosity" value="3" />
<parameter key="random_seed" value="2001" />
<parameter key="send_mail" value="1" />
<parameter key="process_duration_for_mail" value="30" />
<parameter key="encoding" value="SYSTEM" />
- <process expanded="true" height="440" width="560">
- <operator activated="true" class="generate_data" expanded="true" height="60" name="TrainData" width="90" x="45" y="30">
<parameter key="target_function" value="random classification" />
<parameter key="number_examples" value="100" />
<parameter key="number_of_attributes" value="5" />
<parameter key="attributes_lower_bound" value="-10.0" />
<parameter key="attributes_upper_bound" value="10.0" />
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<parameter key="datamanagement" value="0" />
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<parameter key="value_type" value="0" />
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<parameter key="max" value="1.0" />
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<parameter key="name" value="data" />
<parameter key="io_object" value="ExampleSet" />
<parameter key="store_which" value="1" />
<parameter key="remove_from_process" value="false" />
</operator>
- <operator activated="true" class="optimize_parameters_grid" expanded="true" height="148" name="ParameterOptimization" width="90" x="313" y="120">
- <list key="parameters">
<parameter key="Training.gamma" value="[0.0;1024;16;quadratic]" />
<parameter key="Training.C" value="[0.0;1000;10;quadratic]" />
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<parameter key="average_performances_only" value="true" />
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<parameter key="number_of_validations" value="10" />
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<parameter key="kernel_type" value="2" />
<parameter key="degree" value="5" />
<parameter key="gamma" value="1024.0" />
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<parameter key="C" value="1000.0" />
<parameter key="nu" value="0.5" />
<parameter key="cache_size" value="80" />
<parameter key="epsilon" value="0.01" />
<parameter key="p" value="0.1" />
<list key="class_weights" />
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<parameter key="calculate_confidences" value="false" />
<parameter key="confidence_for_multiclass" value="true" />
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<parameter key="kappa" value="false" />
<parameter key="AUC (optimistic)" value="false" />
<parameter key="AUC" value="true" />
<parameter key="AUC (pessimistic)" value="false" />
<parameter key="precision" value="false" />
<parameter key="recall" value="false" />
<parameter key="lift" value="false" />
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<parameter key="f_measure" value="false" />
<parameter key="false_positive" value="false" />
<parameter key="false_negative" value="false" />
<parameter key="true_positive" value="false" />
<parameter key="true_negative" value="false" />
<parameter key="sensitivity" value="false" />
<parameter key="specificity" value="false" />
<parameter key="youden" value="false" />
<parameter key="positive_predictive_value" value="false" />
<parameter key="negative_predictive_value" value="false" />
<parameter key="psep" value="false" />
<parameter key="skip_undefined_labels" value="true" />
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<parameter key="filename" value="paraopt.log" />
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<parameter key="Gamma" value="operator.Training.parameter.gamma" />
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