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forrest.txt
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forrest.txt
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*****NEW RESULT*************************
***forrest_B.arff***
best classifier: weka.classifiers.trees.RandomForest
arguments: [-I, 10, -K, 0, -depth, 0]
attribute search: null
attribute search arguments: []
attribute evaluation: null
attribute evaluation arguments: []
metric: errorRate
estimated errorRate: 0.045454545454545456
training time on evaluation dataset: 0.036 seconds
You can use the chosen classifier in your own code as follows:
Classifier classifier = AbstractClassifier.forName("weka.classifiers.trees.RandomForest", new String[]{"-I", "10", "-K", "0", "-depth", "0"});
classifier.buildClassifier(instances);
Correctly Classified Instances 21 95.4545 %
Incorrectly Classified Instances 1 4.5455 %
Kappa statistic 0.6452
Mean absolute error 0.0591
Root mean squared error 0.1581
Relative absolute error 30.5882 %
Root relative squared error 54.6173 %
Total Number of Instances 22
=== Confusion Matrix ===
a b <-- classified as
20 0 | a = FALSE
1 1 | b = TRUE
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
1.000 0.500 0.952 1.000 0.976 0.690 1.000 1.000 FALSE
0.500 0.000 1.000 0.500 0.667 0.690 1.000 1.000 TRUE
Weighted Avg. 0.955 0.455 0.957 0.955 0.948 0.690 1.000 1.000
Temporary run directories:
/tmp/autoweka2813502742502137239/
For better performance, try giving Auto-WEKA more time.
*********************************************
*****NEW RESULT*************************
***forrest_A.arff***
best classifier: weka.classifiers.trees.RandomForest
arguments: [-I, 8, -K, 0, -depth, 0]
attribute search: weka.attributeSelection.BestFirst
attribute search arguments: [-D, 0, -N, 4]
attribute evaluation: weka.attributeSelection.CfsSubsetEval
attribute evaluation arguments: []
metric: errorRate
estimated errorRate: 0.1111111111111111
training time on evaluation dataset: 0.022 seconds
You can use the chosen classifier in your own code as follows:
AttributeSelection as = new AttributeSelection();
ASSearch asSearch = ASSearch.forName("weka.attributeSelection.BestFirst", new String[]{"-D", "0", "-N", "4"});
as.setSearch(asSearch);
ASEvaluation asEval = ASEvaluation.forName("weka.attributeSelection.CfsSubsetEval", new String[]{});
as.setEvaluator(asEval);
as.SelectAttributes(instances);
instances = as.reduceDimensionality(instances);
Classifier classifier = AbstractClassifier.forName("weka.classifiers.trees.RandomForest", new String[]{"-I", "8", "-K", "0", "-depth", "0"});
classifier.buildClassifier(instances);
Correctly Classified Instances 40 88.8889 %
Incorrectly Classified Instances 5 11.1111 %
Kappa statistic 0.3902
Mean absolute error 0.1642
Root mean squared error 0.296
Relative absolute error 67.7073 %
Root relative squared error 86.9868 %
Total Number of Instances 45
=== Confusion Matrix ===
a b <-- classified as
38 1 | a = FALSE
4 2 | b = TRUE
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.974 0.667 0.905 0.974 0.938 0.419 0.818 0.949 FALSE
0.333 0.026 0.667 0.333 0.444 0.419 0.818 0.445 TRUE
Weighted Avg. 0.889 0.581 0.873 0.889 0.872 0.419 0.818 0.882
Temporary run directories:
/tmp/autoweka6885801059024917742/
For better performance, try giving Auto-WEKA more time.
Tried 642 configurations; to get good results reliably you may need to allow for trying thousands of configurations.
*********************************************
*****NEW RESULT*************************
***forrest_A_O1.arff***
best classifier: weka.classifiers.trees.RandomForest
arguments: [-I, 113, -K, 0, -depth, 17]
attribute search: weka.attributeSelection.GreedyStepwise
attribute search arguments: [-C, -N, 210]
attribute evaluation: weka.attributeSelection.CfsSubsetEval
attribute evaluation arguments: [-M]
metric: errorRate
estimated errorRate: 0.16666666666666666
training time on evaluation dataset: 0.135 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", "-N", "210"});
as.setSearch(asSearch);
ASEvaluation asEval = ASEvaluation.forName("weka.attributeSelection.CfsSubsetEval", new String[]{"-M"});
as.setEvaluator(asEval);
as.SelectAttributes(instances);
instances = as.reduceDimensionality(instances);
Classifier classifier = AbstractClassifier.forName("weka.classifiers.trees.RandomForest", new String[]{"-I", "113", "-K", "0", "-depth", "17"});
classifier.buildClassifier(instances);
Correctly Classified Instances 65 83.3333 %
Incorrectly Classified Instances 13 16.6667 %
Kappa statistic 0.6667
Mean absolute error 0.2259
Root mean squared error 0.3318
Relative absolute error 45.1734 %
Root relative squared error 66.3533 %
Total Number of Instances 78
=== Confusion Matrix ===
a b <-- classified as
26 13 | a = FALSE
0 39 | b = TRUE
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.667 0.000 1.000 0.667 0.800 0.707 0.907 0.904 FALSE
1.000 0.333 0.750 1.000 0.857 0.707 0.907 0.872 TRUE
Weighted Avg. 0.833 0.167 0.875 0.833 0.829 0.707 0.907 0.888
Temporary run directories:
/tmp/autoweka3900346103333215898/
For better performance, try giving Auto-WEKA more time.
Tried 573 configurations; to get good results reliably you may need to allow for trying thousands of configurations.
*********************************************
*****NEW RESULT*************************
***forrest_A_S1.arff***
best classifier: weka.classifiers.trees.RandomForest
arguments: [-I, 29, -K, 0, -depth, 6]
attribute search: weka.attributeSelection.GreedyStepwise
attribute search arguments: [-C, -B, -R]
attribute evaluation: weka.attributeSelection.CfsSubsetEval
attribute evaluation arguments: []
metric: errorRate
estimated errorRate: 0.10256410256410256
training time on evaluation dataset: 0.014 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[]{});
as.setEvaluator(asEval);
as.SelectAttributes(instances);
instances = as.reduceDimensionality(instances);
Classifier classifier = AbstractClassifier.forName("weka.classifiers.trees.RandomForest", new String[]{"-I", "29", "-K", "0", "-depth", "6"});
classifier.buildClassifier(instances);
Correctly Classified Instances 70 89.7436 %
Incorrectly Classified Instances 8 10.2564 %
Kappa statistic 0.7949
Mean absolute error 0.1858
Root mean squared error 0.2719
Relative absolute error 37.1574 %
Root relative squared error 54.3701 %
Total Number of Instances 78
=== Confusion Matrix ===
a b <-- classified as
32 7 | a = FALSE
1 38 | b = TRUE
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.821 0.026 0.970 0.821 0.889 0.804 0.970 0.964 FALSE
0.974 0.179 0.844 0.974 0.905 0.804 0.970 0.960 TRUE
Weighted Avg. 0.897 0.103 0.907 0.897 0.897 0.804 0.970 0.962
Temporary run directories:
/tmp/autoweka7304119559539545496/
For better performance, try giving Auto-WEKA more time.
Tried 627 configurations; to get good results reliably you may need to allow for trying thousands of configurations.
*********************************************
*****NEW RESULT*************************
***forrest_A_U1.arff***
best classifier: weka.classifiers.meta.AdaBoostM1
arguments: [-P, 100, -I, 19, -Q, -S, 1, -W, weka.classifiers.rules.PART, --, -N, 4, -M, 1, -R]
attribute search: weka.attributeSelection.GreedyStepwise
attribute search arguments: [-N, 127]
attribute evaluation: weka.attributeSelection.CfsSubsetEval
attribute evaluation arguments: [-M]
metric: errorRate
estimated errorRate: 0.25
training time on evaluation dataset: 0.023 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[]{"-N", "127"});
as.setSearch(asSearch);
ASEvaluation asEval = ASEvaluation.forName("weka.attributeSelection.CfsSubsetEval", new String[]{"-M"});
as.setEvaluator(asEval);
as.SelectAttributes(instances);
instances = as.reduceDimensionality(instances);
Classifier classifier = AbstractClassifier.forName("weka.classifiers.meta.AdaBoostM1", new String[]{"-P", "100", "-I", "19", "-Q", "-S", "1", "-W", "weka.classifiers.rules.PART", "--", "-N", "4", "-M", "1", "-R"});
classifier.buildClassifier(instances);
Correctly Classified Instances 9 75 %
Incorrectly Classified Instances 3 25 %
Kappa statistic 0.5
Mean absolute error 0.363
Root mean squared error 0.4057
Relative absolute error 72.5971 %
Root relative squared error 81.1369 %
Total Number of Instances 12
=== Confusion Matrix ===
a b <-- classified as
3 3 | a = FALSE
0 6 | b = TRUE
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.500 0.000 1.000 0.500 0.667 0.577 0.792 0.773 FALSE
1.000 0.500 0.667 1.000 0.800 0.577 0.792 0.722 TRUE
Weighted Avg. 0.750 0.250 0.833 0.750 0.733 0.577 0.792 0.747
Temporary run directories:
/tmp/autoweka1552863335856158690/
For better performance, try giving Auto-WEKA more time.
Tried 783 configurations; to get good results reliably you may need to allow for trying thousands of configurations.
*********************************************