lucept2.txt

*****NEW RESULT*************************
***********luce_A_O1.arff*****
best classifier: weka.classifiers.trees.J48
arguments: [-J, -M, 1, -C, 0.5861833183124912]
attribute search: weka.attributeSelection.GreedyStepwise
attribute search arguments: [-C, -B, -R]
attribute evaluation: weka.attributeSelection.CfsSubsetEval
attribute evaluation arguments: [-L]
metric: errorRate
estimated errorRate: 0.06678700361010831
training time on evaluation dataset: 0.202 seconds

You can use the chosen classifier in your own code as follows:

AttributeSelection as = new AttributeSelection();
ASSearch asSearch = ASSearch.forName("weka.attributeSelection.GreedyStepwise", new String[]{"-C", "-B", "-R"});
as.setSearch(asSearch);
ASEvaluation asEval = ASEvaluation.forName("weka.attributeSelection.CfsSubsetEval", new String[]{"-L"});
as.setEvaluator(asEval);
as.SelectAttributes(instances);
instances = as.reduceDimensionality(instances);
Classifier classifier = AbstractClassifier.forName("weka.classifiers.trees.J48", new String[]{"-J", "-M", "1", "-C", "0.5861833183124912"});
classifier.buildClassifier(instances);


Correctly Classified Instances         517               93.3213 %
Incorrectly Classified Instances        37                6.6787 %
Kappa statistic                          0.8664
Mean absolute error                      0.1067
Root mean squared error                  0.231 
Relative absolute error                 21.35   %
Root relative squared error             46.206  %
Total Number of Instances              554     

=== Confusion Matrix ===

   a   b   <-- classified as
 264  13 |   a = FALSE
  24 253 |   b = TRUE

=== Detailed Accuracy By Class ===

                 TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class
                 0.953    0.087    0.917      0.953    0.935      0.867    0.978     0.974     FALSE
                 0.913    0.047    0.951      0.913    0.932      0.867    0.978     0.978     TRUE
Weighted Avg.    0.933    0.067    0.934      0.933    0.933      0.867    0.978     0.976     

Temporary run directories:
/tmp/autoweka943542009170711570/


For better performance, try giving Auto-WEKA more time.
Tried 428 configurations; to get good results reliably you may need to allow for trying thousands of configurations.

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