1.ClassLabels:類型標識.第一個label作為pos,第二次label作為neg.
2.GroundTruth:各次實驗的觀察值,也就是真實值.
3.ValidationCounter: 測試次數
4.SampleDistribution:每個樣本作為測試集樣本的次數.如果是k-fold-validation則會有k次.
5.ErrorDistribution:在測試時每個樣本被誤判的次數
以上2個屬性在k-fold-valiation中可以找出誤判次數多的樣本.
6.SampleDistributionByClass:在測試集中各類樣本數量
7.ErrorDistributionByClass:在被誤判的樣本集中各類樣本的數量
8.CountingMatrix:前2行表示TP,FP;TN,FN;最后一行是inconclusive results.
9.CorrectRate: (TP+FN)/(P+N)
10.ErrorRate:1-CorrectRate
11.Sensitivity: TP/(TP+FP)=recall=FDR(Failure detective rate)
12.Specificity: FN/(TN+FN)=1-FAR(false alarm rate)
13.PositivePredictiveValue:TP/(TP+TN)=precision
14.NegativePredictiveValue:FN/(FP+FN)
15.Prevalence:TP/(TP+FP+TN+FN)
16.DiagnosticTable:與CountingMatrix相同
要求recall,precision,FAR,FDR可以直接在取.