Desglose de Partidos J75
1. BROMMAPOJKARNA-NORRKOPING
REGRESION 1-->0.563
NEURAL 1-->0.543
SMO 1-->0.667
RandomSubSpace 1-->0.553
VIAREGRESION 1-->0.55
FilteredClassifier 1-->0.546
MultiClassClassifier 1-->0.577
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 57.13% PX: 0.00% P2: 0.00%2. VARNAMO-GAIS
REGRESION 2-->0.589
NEURAL 2-->0.578
SMO 2-->0.667
RandomSubSpace 2-->0.576
VIAREGRESION 2-->0.573
FilteredClassifier 2-->0.567
MultiClassClassifier 2-->0.589
Resultado probable: 2
Signos:UNO=0 EQUIS=0 DOS=7Media Probabilidades Modelos:P1: 0.00% PX: 0.00% P2: 59.13%3. DJURGARDENS-HALMSTADS
REGRESION 1-->0.786
NEURAL 1-->0.85
SMO 1-->0.667
RandomSubSpace 1-->0.734
VIAREGRESION 1-->0.779
FilteredClassifier 1-->0.866
MultiClassClassifier 1-->0.78
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 78.03% PX: 0.00% P2: 0.00%4. HACKEN-ELFSBORG
REGRESION 2-->0.434
NEURAL 2-->0.45
SMO 2-->0.667
RandomSubSpace 2-->0.467
VIAREGRESION 2-->0.437
FilteredClassifier 2-->0.526
MultiClassClassifier 2-->0.446
Resultado probable: 2
Signos:UNO=0 EQUIS=0 DOS=7Media Probabilidades Modelos:P1: 0.00% PX: 0.00% P2: 48.96%5. SIRIUS-OSTERS
REGRESION 1-->0.589
NEURAL 1-->0.655
SMO 1-->0.667
RandomSubSpace 1-->0.611
VIAREGRESION 1-->0.576
FilteredClassifier 1-->0.68
MultiClassClassifier 1-->0.597
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 62.50% PX: 0.00% P2: 0.00%6. HAM-KAM-BODOGLIMT
REGRESION 2-->0.715
NEURAL 2-->0.732
SMO 2-->0.667
RandomSubSpace 2-->0.727
VIAREGRESION 2-->0.72
FilteredClassifier 2-->0.74
MultiClassClassifier 2-->0.708
Resultado probable: 2
Signos:UNO=0 EQUIS=0 DOS=7Media Probabilidades Modelos:P1: 0.00% PX: 0.00% P2: 71.56%7. KFUM-ROSENBORG
REGRESION 1-->0.409
NEURAL 1-->0.432
SMO 1-->0.667
RandomSubSpace 1-->0.504
VIAREGRESION 1-->0.413
FilteredClassifier 1-->0.5
MultiClassClassifier 1-->0.417
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 47.74% PX: 0.00% P2: 0.00%8. MOLDE-BRYNE
REGRESION 1-->0.667
NEURAL 1-->0.717
SMO 1-->0.667
RandomSubSpace 1-->0.645
VIAREGRESION 1-->0.651
FilteredClassifier 1-->0.68
MultiClassClassifier 1-->0.673
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 67.14% PX: 0.00% P2: 0.00%9. SARPSBORG-BRANN
REGRESION 1-->0.388
NEURAL 1-->0.385
SMO 1-->0.667
RandomSubSpace 2-->0.632
VIAREGRESION 1-->0.39
FilteredClassifier 1-->0.421
MultiClassClassifier 1-->0.398
Resultado probable: 12
Signos:UNO=6 EQUIS=0 DOS=1Media Probabilidades Modelos:P1: 44.15% PX: 0.00% P2: 63.20%10. VALERENGA-SANDEFJORD
REGRESION 1-->0.462
NEURAL 1-->0.43
SMO 1-->0.667
RandomSubSpace 1-->0.478
VIAREGRESION 1-->0.455
FilteredClassifier 1-->0.5
MultiClassClassifier 1-->0.477
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 49.56% PX: 0.00% P2: 0.00%11. TROMSO-FREDRIKSTAD
REGRESION 1-->0.552
NEURAL 1-->0.527
SMO 1-->0.667
RandomSubSpace 1-->0.366
VIAREGRESION 1-->0.554
FilteredClassifier 1-->0.68
MultiClassClassifier 1-->0.559
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 55.79% PX: 0.00% P2: 0.00%12. STROMSGODSET-HAUGESUND
REGRESION 1-->0.585
NEURAL 1-->0.578
SMO 1-->0.667
RandomSubSpace 1-->0.544
VIAREGRESION 1-->0.568
FilteredClassifier 1-->0.546
MultiClassClassifier 1-->0.595
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 58.33% PX: 0.00% P2: 0.00%13. AARHUS-MIDTJYLLAND
REGRESION 2-->0.387
NEURAL 2-->0.416
SMO 2-->0.667
RandomSubSpace 2-->0.424
VIAREGRESION 2-->0.397
FilteredClassifier 2-->0.526
MultiClassClassifier 2-->0.399
Resultado probable: 2
Signos:UNO=0 EQUIS=0 DOS=7Media Probabilidades Modelos:P1: 0.00% PX: 0.00% P2: 45.94%14. BRONDBY-VIBORG
REGRESION 1-->0.675
NEURAL 1-->0.624
SMO 1-->0.667
RandomSubSpace 1-->0.606
VIAREGRESION 1-->0.663
FilteredClassifier 1-->0.866
MultiClassClassifier 1-->0.68
Resultado probable: 1
Signos:UNO=7 EQUIS=0 DOS=0Media Probabilidades Modelos:P1: 68.30% PX: 0.00% P2: 0.00%