Description: PyCM is a multi-class confusion matrix library written in Python. http://www.pycm.io

# PyCM Report

## Dataset Type :

- Multi-Class Classification
- Balanced

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 | 15 | 1
2 | 0 | 3 | 6

## Overall Statistics :

95% CI | (0.79716,0.99231)
ACC Macro | 0.92982
ARI | 0.73605
AUNP | 0.91458
AUNU | 0.90555
Bangdiwala B | 0.82692
Bennett S | 0.84211
CBA | 0.83333
CSI | 0.76488
Chi-Squared | 53.85218
Chi-Squared DF | 4
Conditional Entropy | 0.35951
Cramer V | 0.84177
Cross Entropy | 1.56133
F1 Macro | 0.87745
F1 Micro | 0.89474
FNR Macro | 0.13194
FNR Micro | 0.10526
FPR Macro | 0.05695
FPR Micro | 0.05263
Gwet AC1 | 0.84537
Hamming Loss | 0.10526
Joint Entropy | 1.90651
KL Divergence | 0.01432
Kappa | 0.8355
Kappa 95% CI | (0.68301,0.98799)
Kappa No Prevalence | 0.78947
Kappa Standard Error | 0.0778
Kappa Unbiased | 0.83514
Krippendorff Alpha | 0.83731
Lambda A | 0.81818
Lambda B | 0.8
Mutual Information | 1.13011
NIR | 0.42105
NPV Macro | 0.95108
NPV Micro | 0.94737
Overall ACC | 0.89474
Overall CEN | 0.17658
Overall J | (2.38947,0.79649)
Overall MCC | 0.83929
Overall MCEN | 0.24726
Overall RACC | 0.36011
Overall RACCU | 0.3615
P-Value | 0.0
PPV Macro | 0.89683
PPV Micro | 0.89474
Pearson C | 0.7657
Phi-Squared | 1.41716
RCI | 0.73051
RR | 12.66667
Reference Entropy | 1.54701
Response Entropy | 1.48962
SOA1(Landis & Koch) | Almost Perfect
SOA2(Fleiss) | Excellent
SOA3(Altman) | Very Good
SOA4(Cicchetti) | Excellent
SOA5(Cramer) | Very Strong
SOA6(Matthews) | Strong
SOA7(Lambda A) | Very Strong
SOA8(Lambda B) | Very Strong
SOA9(Krippendorff Alpha) | High
SOA10(Pearson C) | Strong
Scott PI | 0.83514
Standard Error | 0.04978
TNR Macro | 0.94305
TNR Micro | 0.94737
TPR Macro | 0.86806
TPR Micro | 0.89474
Zero-one Loss | 4

## Class Statistics :

Class | 0 | 1 | 2 | Description
ACC | 1.0 | 0.89474 | 0.89474 | Accuracy
AGF | 1.0 | 0.92297 | 0.799 | Adjusted F-score
AGM | 1.0 | 0.88655 | 0.87294 | Adjusted geometric mean
AM | 0 | 2 | -2 | Difference between automatic and manual classification
AUC | 1.0 | 0.90057 | 0.81609 | Area under the ROC curve
AUCI | Excellent | Excellent | Very Good | AUC value interpretation
AUPR | 1.0 | 0.88542 | 0.7619 | Area under the PR curve
BB | 1.0 | 0.83333 | 0.66667 | Braun-Blanquet similarity
BCD | 0.0 | 0.02632 | 0.02632 | Bray-Curtis dissimilarity
BM | 1.0 | 0.80114 | 0.63218 | Informedness or bookmaker informedness
CEN | 0 | 0.22934 | 0.35141 | Confusion entropy
DOR | None | 95.0 | 56.0 | Diagnostic odds ratio
DP | None | 1.09038 | 0.96383 | Discriminant power
DPI | None | Limited | Poor | Discriminant power interpretation
ERR | 0.0 | 0.10526 | 0.10526 | Error rate
F0.5 | 1.0 | 0.85227 | 0.81081 | F0.5 score
F1 | 1.0 | 0.88235 | 0.75 | F1 score - harmonic mean of precision and sensitivity
F2 | 1.0 | 0.91463 | 0.69767 | F2 score
FDR | 0.0 | 0.16667 | 0.14286 | False discovery rate
FN | 0 | 1 | 3 | False negative/miss/type 2 error
FNR | 0.0 | 0.0625 | 0.33333 | Miss rate or false negative rate
FOR | 0.0 | 0.05 | 0.09677 | False omission rate
FP | 0 | 3 | 1 | False positive/type 1 error/false alarm
FPR | 0.0 | 0.13636 | 0.03448 | Fall-out or false positive rate
G | 1.0 | 0.88388 | 0.75593 | G-measure geometric mean of precision and sensitivity
GI | 1.0 | 0.80114 | 0.63218 | Gini index
GM | 1.0 | 0.89981 | 0.8023 | G-mean geometric mean of specificity and sensitivity
HD | 0 | 4 | 4 | Hamming distance
IBA | 1.0 | 0.86946 | 0.45131 | Index of balanced accuracy
ICSI | 1.0 | 0.77083 | 0.52381 | Individual classification success index
IS | 1.54749 | 0.98489 | 1.85561 | Information score
J | 1.0 | 0.78947 | 0.6 | Jaccard index
LS | 2.92308 | 1.97917 | 3.61905 | Lift score
MCC | 1.0 | 0.79218 | 0.69332 | Matthews correlation coefficient
MCCI | Very Strong | Strong | Moderate | Matthews correlation coefficient interpretation
MCEN | 0 | 0.32202 | 0.42664 | Modified confusion entropy
MK | 1.0 | 0.78333 | 0.76037 | Markedness
N | 25 | 22 | 29 | Condition negative
NLR | 0.0 | 0.07237 | 0.34524 | Negative likelihood ratio
NLRI | Good | Good | Poor | Negative likelihood ratio interpretation
NPV | 1.0 | 0.95 | 0.90323 | Negative predictive value
OC | 1.0 | 0.9375 | 0.85714 | Overlap coefficient
OOC | 1.0 | 0.88388 | 0.75593 | Otsuka-Ochiai coefficient
OP | 1.0 | 0.85373 | 0.71164 | Optimized precision
P | 13 | 16 | 9 | Condition positive or support
PLR | None | 6.875 | 19.33333 | Positive likelihood ratio
PLRI | None | Fair | Good | Positive likelihood ratio interpretation
POP | 38 | 38 | 38 | Population
PPV | 1.0 | 0.83333 | 0.85714 | Precision or positive predictive value
PRE | 0.34211 | 0.42105 | 0.23684 | Prevalence
Q | None | 0.97917 | 0.96491 | Yule Q - coefficient of colligation
QI | None | Strong | Strong | Yule Q interpretation
RACC | 0.11704 | 0.19945 | 0.04363 | Random accuracy
RACCU | 0.11704 | 0.20014 | 0.04432 | Random accuracy unbiased
TN | 25 | 19 | 28 | True negative/correct rejection
TNR | 1.0 | 0.86364 | 0.96552 | Specificity or true negative rate
TON | 25 | 20 | 31 | Test outcome negative
TOP | 13 | 18 | 7 | Test outcome positive
TP | 13 | 15 | 6 | True positive/hit
TPR | 1.0 | 0.9375 | 0.66667 | Sensitivity, recall, hit rate, or true positive rate
Y | 1.0 | 0.80114 | 0.63218 | Youden index
dInd | 0.0 | 0.15 | 0.33511 | Distance index
sInd | 1.0 | 0.89393 | 0.76304 | Similarity index

Generated By PyCM Version 4.2