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log4pt1.txt
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log4pt1.txt
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*****NEW RESULT*************************
******log4j_B.arff*******
best classifier: weka.classifiers.trees.RandomForest
arguments: [-I, 20, -K, 7, -depth, 12]
attribute search: weka.attributeSelection.GreedyStepwise
attribute search arguments: [-C, -R]
attribute evaluation: weka.attributeSelection.CfsSubsetEval
attribute evaluation arguments: [-L]
metric: errorRate
estimated errorRate: 0.006666666666666667
training time on evaluation dataset: 0.055 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", "-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.RandomForest", new String[]{"-I", "20", "-K", "7", "-depth", "12"});
classifier.buildClassifier(instances);
Correctly Classified Instances 149 99.3333 %
Incorrectly Classified Instances 1 0.6667 %
Kappa statistic 0.9488
Mean absolute error 0.0343
Root mean squared error 0.0964
Relative absolute error 24.4015 %
Root relative squared error 36.9521 %
Total Number of Instances 150
=== Confusion Matrix ===
a b <-- classified as
139 0 | a = TRUE
1 10 | b = FALSE
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
1.000 0.091 0.993 1.000 0.996 0.950 1.000 1.000 TRUE
0.909 0.000 1.000 0.909 0.952 0.950 1.000 1.000 FALSE
Weighted Avg. 0.993 0.084 0.993 0.993 0.993 0.950 1.000 1.000
Temporary run directories:
/tmp/autoweka878152220611962699/
For better performance, try giving Auto-WEKA more time.
*********************************************
*****NEW RESULT*************************
********log4j_A.arff***********
best classifier: weka.classifiers.trees.J48
arguments: [-O, -B, -J, -A, -S, -M, 16]
attribute search: weka.attributeSelection.GreedyStepwise
attribute search arguments: [-C, -R]
attribute evaluation: weka.attributeSelection.CfsSubsetEval
attribute evaluation arguments: [-L]
metric: errorRate
estimated errorRate: 0.2408026755852843
training time on evaluation dataset: 0.02 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", "-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[]{"-O", "-B", "-J", "-A", "-S", "-M", "16"});
classifier.buildClassifier(instances);
Correctly Classified Instances 227 75.9197 %
Incorrectly Classified Instances 72 24.0803 %
Kappa statistic 0.4738
Mean absolute error 0.3598
Root mean squared error 0.4221
Relative absolute error 74.6614 %
Root relative squared error 85.99 %
Total Number of Instances 299
=== Confusion Matrix ===
a b <-- classified as
161 17 | a = FALSE
55 66 | b = TRUE
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.904 0.455 0.745 0.904 0.817 0.493 0.737 0.739 FALSE
0.545 0.096 0.795 0.545 0.647 0.493 0.737 0.667 TRUE
Weighted Avg. 0.759 0.309 0.766 0.759 0.748 0.493 0.737 0.710
Temporary run directories:
/tmp/autoweka585277403022183872/
For better performance, try giving Auto-WEKA more time.
Tried 786 configurations; to get good results reliably you may need to allow for trying thousands of configurations.
*********************************************