Entrepot de données
Partie 1: Classification supervisée
Un premier ensemble d'exemples simples
*Iris
| |Naive Bayes |K-NN |C4.5 |SVM |
|TNI |51 |51 |51 |51 |
|CCI |48 → 96.0784 % |49 → 96.0784 % |49 → 96.0784 % |49 → 96.0784 % |
|ICI |3 → 5.8824 % |2 → 3.9216 % |2 → 3.9216 % |2 → 3.9216 % |
=>Naive Bayes
Correctly Classified Instances 48 94.1176 %
Incorrectly Classified Instances 3 5.8824 %
Kappa statistic 0.9113
Mean absolute error 0.0447
Root mean squared error 0.1722
Relative absolute error 10.0365 %
Root relative squared error 36.4196 %
Coverage of cases (0.95 level) 98.0392 %
Mean rel. region size (0.95 level) 37.2549 %
Total Number of Instances 51
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure ROC Area Class 1 0 1 1 1 1 Iris-setosa 0.947 0.063 0.9 0.947 0.923 0.988 Iris-versicolor 0.882 0.029 0.938 0.882 0.909 0.988 Iris-virginica
Weighted Avg. 0.941 0.033 0.942 0.941 0.941 0.992
=== Confusion Matrix ===
a b c K-NN
Correctly Classified Instances 49 96.0784 %
Incorrectly Classified Instances 2 3.9216 %
Kappa statistic 0.9408
Mean absolute error