PyCM Report

Confusion Matrix :

Actual Predict
0 1 2
0 3 0 2
1 0 1 1
2 0 2 3

Overall Statistics :

95% CI (0.30439,0.86228)
Bennett_S 0.375
Chi-Squared 6.6
Chi-Squared DF 4
Conditional Entropy 0.97579
Cramer_V 0.5244
Cross Entropy 1.58333
Gwet_AC1 0.38931
Hamming Loss 0.41667
Joint Entropy 2.45915
KL Divergence 0.09998
Kappa 0.35484
Kappa 95% CI (-0.07708,0.78675)
Kappa No Prevalence 0.16667
Kappa Standard Error 0.22036
Kappa Unbiased 0.34426
Lambda A 0.42857
Lambda B 0.16667
Mutual Information 0.52421
NIR 0.41667
Overall_ACC 0.58333
Overall_CEN 0.46381
Overall_J (1.225,0.40833)
Overall_MCEN 0.51894
Overall_RACC 0.35417
Overall_RACCU 0.36458
P-Value 0.18926
PPV_Macro 0.61111
PPV_Micro 0.58333
Phi-Squared 0.55
Reference Entropy 1.48336
Response Entropy 1.5
Scott_PI 0.34426
Standard Error 0.14232
Strength_Of_Agreement(Altman) Fair
Strength_Of_Agreement(Cicchetti) Poor
Strength_Of_Agreement(Fleiss) Poor
Strength_Of_Agreement(Landis and Koch) Fair
TPR_Macro 0.56667
TPR_Micro 0.58333
Zero-one Loss 5

Class Statistics :

Class 0 1 2 Description
ACC 0.83333 0.75 0.58333 Accuracy
BM 0.6 0.3 0.17143 Informedness or bookmaker informedness
CEN 0.25 0.49658 0.60442 Confusion entropy
DOR None 4.0 2.0 Diagnostic odds ratio
ERR 0.16667 0.25 0.41667 Error rate
F0.5 0.88235 0.35714 0.51724 F0.5 score
F1 0.75 0.4 0.54545 F1 score - harmonic mean of precision and sensitivity
F2 0.65217 0.45455 0.57692 F2 score
FDR 0.0 0.66667 0.5 False discovery rate
FN 2 1 2 False negative/miss/type 2 error
FNR 0.4 0.5 0.4 Miss rate or false negative rate
FOR 0.22222 0.11111 0.33333 False omission rate
FP 0 2 3 False positive/type 1 error/false alarm
FPR 0.0 0.2 0.42857 Fall-out or false positive rate
G 0.7746 0.40825 0.54772 G-measure geometric mean of precision and sensitivity
IS 1.26303 1.0 0.26303 Information score
J 0.6 0.25 0.375 Jaccard index
LR+ None 2.5 1.4 Positive likelihood ratio
LR- 0.4 0.625 0.7 Negative likelihood ratio
MCC 0.68313 0.2582 0.16903 Matthews correlation coefficient
MCEN 0.26439 0.5 0.6875 Modified confusion entropy
MK 0.77778 0.22222 0.16667 Markedness
N 7 10 7 Condition negative
NPV 0.77778 0.88889 0.66667 Negative predictive value
P 5 2 5 Condition positive or support
POP 12 12 12 Population
PPV 1.0 0.33333 0.5 Precision or positive predictive value
PRE 0.41667 0.16667 0.41667 Prevalence
RACC 0.10417 0.04167 0.20833 Random accuracy
RACCU 0.11111 0.0434 0.21007 Random accuracy unbiased
TN 7 8 4 True negative/correct rejection
TNR 1.0 0.8 0.57143 Specificity or true negative rate
TON 9 9 6 Test outcome negative
TOP 3 3 6 Test outcome positive
TP 3 1 3 True positive/hit
TPR 0.6 0.5 0.6 Sensitivity, recall, hit rate, or true positive rate

Generated By PyCM Version 1.3