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
L1 L2 L3
L1 3 0 2
L2 0 1 1
L3 0 2 3

Overall Statistics :

95% CI (0.30439,0.86228)
ACC Macro 0.72222
ARI 0.09206
AUNP 0.68571
AUNU 0.67857
Bangdiwala B 0.37255
Bennett S 0.375
CBA 0.47778
CSI 0.17778
Chi-Squared 6.6
Chi-Squared DF 4
Conditional Entropy 0.97579
Cramer V 0.5244
Cross Entropy 1.58333
F1 Macro 0.56515
F1 Micro 0.58333
FNR Macro 0.43333
FNR Micro 0.41667
FPR Macro 0.20952
FPR Micro 0.20833
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
Krippendorff Alpha 0.37158
Lambda A 0.42857
Lambda B 0.16667
Mutual Information 0.52421
NIR 0.41667
NPV Macro 0.77778
NPV Micro 0.79167
Overall ACC 0.58333
Overall CEN 0.46381
Overall J (1.225,0.40833)
Overall MCC 0.36667
Overall MCEN 0.51894
Overall RACC 0.35417
Overall RACCU 0.36458
P-Value 0.18926
PPV Macro 0.61111
PPV Micro 0.58333
Pearson C 0.59568
Phi-Squared 0.55
RCI 0.35339
RR 4.0
Reference Entropy 1.48336
Response Entropy 1.5
SOA1(Landis & Koch) Fair
SOA2(Fleiss) Poor
SOA3(Altman) Fair
SOA4(Cicchetti) Poor
SOA5(Cramer) Relatively Strong
SOA6(Matthews) Weak
SOA7(Lambda A) Moderate
SOA8(Lambda B) Very Weak
SOA9(Krippendorff Alpha) Low
SOA10(Pearson C) Strong
Scott PI 0.34426
Standard Error 0.14232
TNR Macro 0.79048
TNR Micro 0.79167
TPR Macro 0.56667
TPR Micro 0.58333
Zero-one Loss 5

Class Statistics :

Class L1 L2 L3 Description
ACC 0.83333 0.75 0.58333 Accuracy
AGF 0.72859 0.62869 0.61009 Adjusted F-score
AGM 0.85764 0.70861 0.58034 Adjusted geometric mean
AM -2 1 1 Difference between automatic and manual classification
AUC 0.8 0.65 0.58571 Area under the ROC curve
AUCI Very Good Fair Poor AUC value interpretation
AUPR 0.8 0.41667 0.55 Area under the PR curve
BB 0.6 0.33333 0.5 Braun-Blanquet similarity
BCD 0.08333 0.04167 0.04167 Bray-Curtis dissimilarity
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
DP None 0.33193 0.16597 Discriminant power
DPI None Poor Poor Discriminant power interpretation
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
GI 0.6 0.3 0.17143 Gini index
GM 0.7746 0.63246 0.58554 G-mean geometric mean of specificity and sensitivity
HD 2 3 5 Hamming distance
IBA 0.36 0.28 0.35265 Index of balanced accuracy
ICSI 0.6 -0.16667 0.1 Individual classification success index
IS 1.26303 1.0 0.26303 Information score
J 0.6 0.25 0.375 Jaccard index
LS 2.4 2.0 1.2 Lift score
MCC 0.68313 0.2582 0.16903 Matthews correlation coefficient
MCCI Moderate Negligible Negligible Matthews correlation coefficient interpretation
MCEN 0.26439 0.5 0.6875 Modified confusion entropy
MK 0.77778 0.22222 0.16667 Markedness
N 7 10 7 Condition negative
NLR 0.4 0.625 0.7 Negative likelihood ratio
NLRI Poor Negligible Negligible Negative likelihood ratio interpretation
NPV 0.77778 0.88889 0.66667 Negative predictive value
OC 1.0 0.5 0.6 Overlap coefficient
OOC 0.7746 0.40825 0.54772 Otsuka-Ochiai coefficient
OP 0.58333 0.51923 0.55894 Optimized precision
P 5 2 5 Condition positive or support
PLR None 2.5 1.4 Positive likelihood ratio
PLRI None Poor Poor Positive likelihood ratio interpretation
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
Q None 0.6 0.33333 Yule Q - coefficient of colligation
QI None Moderate Weak Yule Q interpretation
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
Y 0.6 0.3 0.17143 Youden index
dInd 0.4 0.53852 0.58624 Distance index
sInd 0.71716 0.61921 0.58547 Similarity index

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