Classifier: RIMARC Domain: sampleDataSet.txt Verbosity: 3 First: 2 instances will be used for testing. Number of instances: 15 Number of features: 3 Training with instances 2..14 Class counts in the training set: P=4 N=9 categoricF C 0.94444454 green score=1.0, counts: P=3 N=0 yellow score=0.33333334, counts: P=1 N=2 red score=0.0, counts: P=0 N=2 blue score=0.0, counts: P=0 N=2 white score=0.0, counts: P=0 N=3 ordinalF N 0.44444442 2.5..5.5 score=0.44444445, counts: P=4 N=5 <2.5 score=0.0, counts: P=0 N=3 5.5< score=0.0, counts: P=0 N=1 numericalF N 1.0 8.32..88.5 score=1.0, counts: P=4 N=0 <8.32 score=0.0, counts: P=0 N=7 88.5< score=0.0, counts: P=0 N=2 Rules learned after training: categoricF C 0.94444454 green score=1.0, counts: P=3 N=0 yellow score=0.33333334, counts: P=1 N=2 red score=0.0, counts: P=0 N=2 blue score=0.0, counts: P=0 N=2 white score=0.0, counts: P=0 N=3 ordinalF N 0.44444442 2.5..5.5 score=0.44444445, counts: P=4 N=5 <2.5 score=0.0, counts: P=0 N=3 5.5< score=0.0, counts: P=0 N=1 numericalF N 1.0 8.32..88.5 score=1.0, counts: P=4 N=0 <8.32 score=0.0, counts: P=0 N=7 88.5< score=0.0, counts: P=0 N=2 Computing scores of instances 0..1 Computing score for instance 0 (class: N) categoricF=red weight=0.94444454 score=0.0 ordinalF=2.0 (<2.5) weight=0.44444442 score=0.0 numericalF=-0.5 (<8.32) weight=1.0 score=0.0 TotalScore=0.0 TotalWeight=2.3888888 Instance score= 0.0 Computing score for instance 1 (class: P) categoricF=green weight=0.94444454 score=1.0 ordinalF=3.0 (2.5..5.5) weight=0.44444442 score=0.44444445 numericalF=10.7 (8.32..88.5) weight=1.0 score=1.0 TotalScore=2.1419754 TotalWeight=2.3888888 Instance score= 0.8966409 Sorted curve points: No: FPR TPR 0: 0.0 0.0 1: 0.0 1.0 2: 1.0 1.0 AUC: 1.0 Time to learn the model: 0 ms. Time to test the model: 0 ms.