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

# PyCM Report

## Dataset Type :

- Balanced
- Multi-Class Classification

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 | 1 | 8

## Overall Statistics :

95% CI | (0.87637,1.01837)
ACC Macro | 0.96491
ARI | 0.85267
AUNP | 0.96003
AUNU | 0.95774
Bangdiwala B | 0.90514
Bennett S | 0.92105
CBA | 0.94213
CSI | 0.88426
Chi-Squared | 63.95091
Chi-Squared DF | 4
Conditional Entropy | 0.26121
Cramer V | 0.91731
Cross Entropy | 1.54701
F1 Macro | 0.94213
F1 Micro | 0.94737
FNR Macro | 0.05787
FNR Micro | 0.05263
FPR Macro | 0.02665
FPR Micro | 0.02632
Gwet AC1 | 0.92205
Hamming Loss | 0.05263
Joint Entropy | 1.80822
KL Divergence | 0.0
Kappa | 0.91898
Kappa 95% CI | (0.80968,1.02827)
Kappa No Prevalence | 0.89474
Kappa Standard Error | 0.05576
Kappa Unbiased | 0.91898
Krippendorff Alpha | 0.92004
Lambda A | 0.90909
Lambda B | 0.90909
Mutual Information | 1.2858
NIR | 0.42105
NPV Macro | 0.97335
NPV Micro | 0.97368
Overall ACC | 0.94737
Overall CEN | 0.12066
Overall J | (2.68235,0.89412)
Overall MCC | 0.91898
Overall MCEN | 0.18523
Overall RACC | 0.35042
Overall RACCU | 0.35042
P-Value | 0.0
PPV Macro | 0.94213
PPV Micro | 0.94737
Pearson C | 0.792
Phi-Squared | 1.68292
RCI | 0.83115
RR | 12.66667
Reference Entropy | 1.54701
Response Entropy | 1.54701
SOA1(Landis & Koch) | Almost Perfect
SOA2(Fleiss) | Excellent
SOA3(Altman) | Very Good
SOA4(Cicchetti) | Excellent
SOA5(Cramer) | Very Strong
SOA6(Matthews) | Very Strong
SOA7(Lambda A) | Very Strong
SOA8(Lambda B) | Very Strong
SOA9(Krippendorff Alpha) | High
SOA10(Pearson C) | Strong
Scott PI | 0.91898
Standard Error | 0.03622
TNR Macro | 0.97335
TNR Micro | 0.97368
TPR Macro | 0.94213
TPR Micro | 0.94737
Zero-one Loss | 2

## Class Statistics :

Class | 0 | 1 | 2 | Description
ACC | 1.0 | 0.94737 | 0.94737 | Accuracy
AGF | 1.0 | 0.94598 | 0.92641 | Adjusted F-score
AGM | 1.0 | 0.94912 | 0.94334 | Adjusted geometric mean
AM | 0 | 0 | 0 | Difference between automatic and manual classification
AUC | 1.0 | 0.94602 | 0.9272 | Area under the ROC curve
AUCI | Excellent | Excellent | Excellent | AUC value interpretation
AUPR | 1.0 | 0.9375 | 0.88889 | Area under the PR curve
BB | 1.0 | 0.9375 | 0.88889 | Braun-Blanquet similarity
BCD | 0.0 | 0.0 | 0.0 | Bray-Curtis dissimilarity
BM | 1.0 | 0.89205 | 0.85441 | Informedness or bookmaker informedness
CEN | 0 | 0.15625 | 0.23166 | Confusion entropy
DOR | None | 315.0 | 224.0 | Diagnostic odds ratio
DP | None | 1.37739 | 1.29576 | Discriminant power
DPI | None | Limited | Limited | Discriminant power interpretation
ERR | 0.0 | 0.05263 | 0.05263 | Error rate
F0.5 | 1.0 | 0.9375 | 0.88889 | F0.5 score
F1 | 1.0 | 0.9375 | 0.88889 | F1 score - harmonic mean of precision and sensitivity
F2 | 1.0 | 0.9375 | 0.88889 | F2 score
FDR | 0.0 | 0.0625 | 0.11111 | False discovery rate
FN | 0 | 1 | 1 | False negative/miss/type 2 error
FNR | 0.0 | 0.0625 | 0.11111 | Miss rate or false negative rate
FOR | 0.0 | 0.04545 | 0.03448 | False omission rate
FP | 0 | 1 | 1 | False positive/type 1 error/false alarm
FPR | 0.0 | 0.04545 | 0.03448 | Fall-out or false positive rate
G | 1.0 | 0.9375 | 0.88889 | G-measure geometric mean of precision and sensitivity
GI | 1.0 | 0.89205 | 0.85441 | Gini index
GM | 1.0 | 0.94598 | 0.92641 | G-mean geometric mean of specificity and sensitivity
HD | 0 | 2 | 2 | Hamming distance
IBA | 1.0 | 0.87963 | 0.79247 | Index of balanced accuracy
ICSI | 1.0 | 0.875 | 0.77778 | Individual classification success index
IS | 1.54749 | 1.15482 | 1.90808 | Information score
J | 1.0 | 0.88235 | 0.8 | Jaccard index
LS | 2.92308 | 2.22656 | 3.75309 | Lift score
MCC | 1.0 | 0.89205 | 0.85441 | Matthews correlation coefficient
MCCI | Very Strong | Strong | Strong | Matthews correlation coefficient interpretation
MCEN | 0 | 0.24044 | 0.33219 | Modified confusion entropy
MK | 1.0 | 0.89205 | 0.85441 | Markedness
N | 25 | 22 | 29 | Condition negative
NLR | 0.0 | 0.06548 | 0.11508 | Negative likelihood ratio
NLRI | Good | Good | Fair | Negative likelihood ratio interpretation
NPV | 1.0 | 0.95455 | 0.96552 | Negative predictive value
OC | 1.0 | 0.9375 | 0.88889 | Overlap coefficient
OOC | 1.0 | 0.9375 | 0.88889 | Otsuka-Ochiai coefficient
OP | 1.0 | 0.93836 | 0.90605 | Optimized precision
P | 13 | 16 | 9 | Condition positive or support
PLR | None | 20.625 | 25.77778 | Positive likelihood ratio
PLRI | None | Good | Good | Positive likelihood ratio interpretation
POP | 38 | 38 | 38 | Population
PPV | 1.0 | 0.9375 | 0.88889 | Precision or positive predictive value
PR | 0.34211 | 0.42105 | 0.23684 | Positive rate
PRE | 0.34211 | 0.42105 | 0.23684 | Prevalence
Q | None | 0.99367 | 0.99111 | Yule Q - coefficient of colligation
QI | None | Strong | Strong | Yule Q interpretation
RACC | 0.11704 | 0.17729 | 0.05609 | Random accuracy
RACCU | 0.11704 | 0.17729 | 0.05609 | Random accuracy unbiased
TN | 25 | 21 | 28 | True negative/correct rejection
TNR | 1.0 | 0.95455 | 0.96552 | Specificity or true negative rate
TON | 25 | 22 | 29 | Test outcome negative
TOP | 13 | 16 | 9 | Test outcome positive
TOPR | 0.34211 | 0.42105 | 0.23684 | Test outcome positive rate
TP | 13 | 15 | 8 | True positive/hit
TPR | 1.0 | 0.9375 | 0.88889 | Sensitivity, recall, hit rate, or true positive rate
Y | 1.0 | 0.89205 | 0.85441 | Youden index
dInd | 0.0 | 0.07728 | 0.11634 | Distance index
sInd | 1.0 | 0.94535 | 0.91774 | Similarity index

Generated By PyCM Version 4.6