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.