base line: use all the attributes, tree algorithm, cross-validation parameter 3:
| |
true 0 |
true 1 |
class precision |
| pred. 0 |
284253 |
132 |
99.95% |
| pred. 1 |
62 |
360 |
85.31% |
| class recall |
99.98% |
73.17% |
|
PerformanceVector:
accuracy: 99.93% +/- 0.01% (mikro: 99.93%)
ConfusionMatrix:
True: 0 1
0: 284253 132
1: 62 360
precision: 85.68% +/- 6.02% (mikro: 85.31%) (positive class: 1)
ConfusionMatrix:
True: 0 1
0: 284253 132
1: 62 360
recall: 73.14% +/- 6.06% (mikro: 73.17%) (positive class: 1)
ConfusionMatrix:
True: 0 1
0: 284253 132
1: 62 360
AUC (optimistic): 0.965 +/- 0.028 (mikro: 0.965) (positive class: 1)
AUC: 0.858 +/- 0.035 (mikro: 0.858) (positive class: 1)
AUC (pessimistic): 0.752 +/- 0.057 (mikro: 0.752) (positive class: 1)
Use random forest tree:
PerformanceVector:
accuracy: 99.87% +/- 0.01% (mikro: 99.87%)
ConfusionMatrix:
True: 0 1
0: 284298 342
1: 17 150
precision: 93.21% +/- 7.43% (mikro: 89.82%) (positive class: 1)
ConfusionMatrix:
True: 0 1
0: 284298 342
1: 17 150
recall: 30.47% +/- 12.44% (mikro: 30.49%) (positive class: 1)
ConfusionMatrix:
True: 0 1
0: 284298 342
1: 17 150
AUC (optimistic): 0.992 +/- 0.010 (mikro: 0.992) (positive class: 1)
AUC: 0.852 +/- 0.039 (mikro: 0.852) (positive class: 1)
AUC (pessimistic): 0.713 +/- 0.081 (mikro: 0.713) (positive class: 1)
