PyCM Report

Dataset Type :

Note 1 : Recommended statistics for this type of classification highlighted in aqua

Note 2 : The recommendation system assumes the input is the result of classification over the entire dataset, not just a subset. If the confusion matrix is based on test data classification, the recommendation may not be valid.

Confusion Matrix :

Actual Predict
0 1 2
0 13 0 0
1 0 10 6
2 0 0 9

Overall Statistics :

95% CI (0.72617,0.95804)
ACC Macro 0.89474
ARI 0.63251
AUNP 0.89655
AUNU 0.90302
Bangdiwala B 0.75431
Bennett S 0.76316
CBA 0.74167
CSI 0.74167
Chi-Squared 52.25
Chi-Squared DF 4
Conditional Entropy 0.40187
Cramer V 0.82916
Cross Entropy 1.65796
F1 Macro 0.83974
F1 Micro 0.84211
FNR Macro 0.125
FNR Micro 0.15789
FPR Macro 0.06897
FPR Micro 0.07895
Gwet AC1 0.76324
Hamming Loss 0.15789
Joint Entropy 1.94887
KL Divergence 0.11096
Kappa 0.76735
Kappa 95% CI (0.59651,0.93818)
Kappa No Prevalence 0.68421
Kappa Standard Error 0.08716
Kappa Unbiased 0.76299
Krippendorff Alpha 0.76611
Lambda A 0.72727
Lambda B 0.73913
Mutual Information 1.16374
NIR 0.42105
NPV Macro 0.92857
NPV Micro 0.92105
Overall ACC 0.84211
Overall CEN 0.16245
Overall J (2.225,0.74167)
Overall MCC 0.79663
Overall MCEN 0.18661
Overall RACC 0.32133
Overall RACCU 0.3338
P-Value 0.0
PPV Macro 0.86667
PPV Micro 0.84211
Pearson C 0.76089
Phi-Squared 1.375
RCI 0.75225
RR 12.66667
Reference Entropy 1.54701
Response Entropy 1.5656
SOA1(Landis & Koch) Substantial
SOA2(Fleiss) Excellent
SOA3(Altman) Good
SOA4(Cicchetti) Excellent
SOA5(Cramer) Very Strong
SOA6(Matthews) Strong
SOA7(Lambda A) Strong
SOA8(Lambda B) Strong
SOA9(Krippendorff Alpha) Tentative
SOA10(Pearson C) Strong
Scott PI 0.76299
Standard Error 0.05915
TNR Macro 0.93103
TNR Micro 0.92105
TPR Macro 0.875
TPR Micro 0.84211
Zero-one Loss 6

Class Statistics :

Class 0 1 2 Description
ACC 1.0 0.84211 0.84211 Accuracy
AGF 1.0 0.74475 0.91575 Adjusted F-score
AGM 1.0 0.86736 0.84838 Adjusted geometric mean
AM 0 -6 6 Difference between automatic and manual classification
AUC 1.0 0.8125 0.89655 Area under the ROC curve
AUCI Excellent Very Good Very Good AUC value interpretation
AUPR 1.0 0.8125 0.8 Area under the PR curve
BB 1.0 0.625 0.6 Braun-Blanquet similarity
BCD 0.0 0.07895 0.07895 Bray-Curtis dissimilarity
BM 1.0 0.625 0.7931 Informedness or bookmaker informedness
CEN 0 0.24409 0.25 Confusion entropy
DOR None None None Diagnostic odds ratio
DP None None None Discriminant power
DPI None None None Discriminant power interpretation
ERR 0.0 0.15789 0.15789 Error rate
F0.5 1.0 0.89286 0.65217 F0.5 score
F1 1.0 0.76923 0.75 F1 score - harmonic mean of precision and sensitivity
F2 1.0 0.67568 0.88235 F2 score
FDR 0.0 0.0 0.4 False discovery rate
FN 0 6 0 False negative/miss/type 2 error
FNR 0.0 0.375 0.0 Miss rate or false negative rate
FOR 0.0 0.21429 0.0 False omission rate
FP 0 0 6 False positive/type 1 error/false alarm
FPR 0.0 0.0 0.2069 Fall-out or false positive rate
G 1.0 0.79057 0.7746 G-measure geometric mean of precision and sensitivity
GI 1.0 0.625 0.7931 Gini index
GM 1.0 0.79057 0.89056 G-mean geometric mean of specificity and sensitivity
HD 0 6 6 Hamming distance
IBA 1.0 0.39062 0.95719 Index of balanced accuracy
ICSI 1.0 0.625 0.6 Individual classification success index
IS 1.54749 1.24793 1.34104 Information score
J 1.0 0.625 0.6 Jaccard index
LS 2.92308 2.375 2.53333 Lift score
MCC 1.0 0.70076 0.68983 Matthews correlation coefficient
MCCI Very Strong Strong Moderate Matthews correlation coefficient interpretation
MCEN 0 0.26532 0.26439 Modified confusion entropy
MK 1.0 0.78571 0.6 Markedness
N 25 22 29 Condition negative
NLR 0.0 0.375 0.0 Negative likelihood ratio
NLRI Good Poor Good Negative likelihood ratio interpretation
NPV 1.0 0.78571 1.0 Negative predictive value
OC 1.0 1.0 1.0 Overlap coefficient
OOC 1.0 0.79057 0.7746 Otsuka-Ochiai coefficient
OP 1.0 0.61134 0.72672 Optimized precision
P 13 16 9 Condition positive or support
PLR None None 4.83333 Positive likelihood ratio
PLRI None None Poor Positive likelihood ratio interpretation
POP 38 38 38 Population
PPV 1.0 1.0 0.6 Precision or positive predictive value
PRE 0.34211 0.42105 0.23684 Prevalence
Q None None None Yule Q - coefficient of colligation
QI None None None Yule Q interpretation
RACC 0.11704 0.1108 0.09349 Random accuracy
RACCU 0.11704 0.11704 0.09972 Random accuracy unbiased
TN 25 22 23 True negative/correct rejection
TNR 1.0 1.0 0.7931 Specificity or true negative rate
TON 25 28 23 Test outcome negative
TOP 13 10 15 Test outcome positive
TP 13 10 9 True positive/hit
TPR 1.0 0.625 1.0 Sensitivity, recall, hit rate, or true positive rate
Y 1.0 0.625 0.7931 Youden index
dInd 0.0 0.375 0.2069 Distance index
sInd 1.0 0.73483 0.8537 Similarity index

Generated By PyCM Version 4.2