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Table 6. 
Classification results − best parameter settings (on training data set)
Model+1 (best parameter)+1 (min recall 0.6)
PrecisionRecallF1AccuracyPrecisionRecallF1Accuracy
Logistic 0.9328 0.7099 0.8062 0.9860 0.9644 0.6558 0.7807 0.9848 
Random Forest 0.9457 0.8520 0.8964 0.9919 0.9616 0.8287 0.8902 0.9916 
AdaBoost 0.9246 0.8602 0.8912 0.9914 0.9268 0.8534 0.8886 0.9912 
Heuristic 0.8991 0.6786 0.7734 0.9826 0.8991 0.6786 0.7734 0.9826 
Model+1 (best parameter)+1 (min recall 0.6)
PrecisionRecallF1AccuracyPrecisionRecallF1Accuracy
Logistic 0.9328 0.7099 0.8062 0.9860 0.9644 0.6558 0.7807 0.9848 
Random Forest 0.9457 0.8520 0.8964 0.9919 0.9616 0.8287 0.8902 0.9916 
AdaBoost 0.9246 0.8602 0.8912 0.9914 0.9268 0.8534 0.8886 0.9912 
Heuristic 0.8991 0.6786 0.7734 0.9826 0.8991 0.6786 0.7734 0.9826 
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