"Visualisation Performance Binominal Classification"

DocMusher

Hi,
I would like to know if anyone has experience with the representation of the results of a NB classification (Binominal Classification)
I find the PerformanceVector (cfr infra) but the plot view is not very illustrative. Anyone experience with a nice way to present the performance vector in another way?
Cheers
Sven

PerformanceVector:
accuracy: 89.04%
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
classification_error: 10.96%
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
kappa: 0.514
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
precision: 97.80% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
recall: 89.96% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
lift: 107.63% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
fallout: 20.11% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
f_measure: 93.72% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
false_positive: 2189.000 (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
false_negative: 10873.000 (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
true_positive: 97407.000 (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
true_negative: 8695.000 (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
sensitivity: 89.96% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
specificity: 79.89% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
youden: 0.698 (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
positive_predictive_value: 97.80% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
negative_predictive_value: 44.43% (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407
psep: 0.422 (positive class: N)
ConfusionMatrix:
True: Y N
Y: 8695 10873
N: 2189 97407