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 | 0 | 9

## Overall Statistics :

95% CI | (0.92279,1.02458)
ACC Macro | 0.98246
ARI | 0.92263
AUNP | 0.98276
AUNU | 0.98384
Bangdiwala B | 0.9519
Bennett S | 0.96053
CBA | 0.94583
CSI | 0.94583
Chi-Squared | 70.0625
Chi-Squared DF | 4
Conditional Entropy | 0.14202
Cramer V | 0.96014
Cross Entropy | 1.55021
F1 Macro | 0.9717
F1 Micro | 0.97368
FNR Macro | 0.02083
FNR Micro | 0.02632
FPR Macro | 0.01149
FPR Micro | 0.01316
Gwet AC1 | 0.9609
Hamming Loss | 0.02632
Joint Entropy | 1.68902
KL Divergence | 0.0032
Kappa | 0.95979
Kappa 95% CI | (0.88202,1.03756)
Kappa No Prevalence | 0.94737
Kappa Standard Error | 0.03968
Kappa Unbiased | 0.95977
Krippendorff Alpha | 0.9603
Lambda A | 0.95455
Lambda B | 0.95652
Mutual Information | 1.42359
NIR | 0.42105
NPV Macro | 0.98551
NPV Micro | 0.98684
Overall ACC | 0.97368
Overall CEN | 0.06054
Overall J | (2.8375,0.94583)
Overall MCC | 0.96082
Overall MCEN | 0.09387
Overall RACC | 0.34557
Overall RACCU | 0.34591
P-Value | 0.0
PPV Macro | 0.96667
PPV Micro | 0.97368
Pearson C | 0.8052
Phi-Squared | 1.84375
RCI | 0.92022
RR | 12.66667
Reference Entropy | 1.54701
Response Entropy | 1.5656
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 | 0.95977
Standard Error | 0.02597
TNR Macro | 0.98851
TNR Micro | 0.98684
TPR Macro | 0.97917
TPR Micro | 0.97368
Zero-one Loss | 1

## Class Statistics :

Class | 0 | 1 | 2 | Description
ACC | 1.0 | 0.97368 | 0.97368 | Accuracy
AGF | 1.0 | 0.95711 | 0.98556 | Adjusted F-score
AGM | 1.0 | 0.97989 | 0.97521 | Adjusted geometric mean
AM | 0 | -1 | 1 | Difference between automatic and manual classification
AUC | 1.0 | 0.96875 | 0.98276 | Area under the ROC curve
AUCI | Excellent | Excellent | Excellent | AUC value interpretation
AUPR | 1.0 | 0.96875 | 0.95 | Area under the PR curve
BB | 1.0 | 0.9375 | 0.9 | Braun-Blanquet similarity
BCD | 0.0 | 0.01316 | 0.01316 | Bray-Curtis dissimilarity
BM | 1.0 | 0.9375 | 0.96552 | Informedness or bookmaker informedness
CEN | 0 | 0.07991 | 0.11179 | 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 | 0.02632 | 0.02632 | Error rate
F0.5 | 1.0 | 0.98684 | 0.91837 | F0.5 score
F1 | 1.0 | 0.96774 | 0.94737 | F1 score - harmonic mean of precision and sensitivity
F2 | 1.0 | 0.94937 | 0.97826 | F2 score
FDR | 0.0 | 0.0 | 0.1 | False discovery rate
FN | 0 | 1 | 0 | False negative/miss/type 2 error
FNR | 0.0 | 0.0625 | 0.0 | Miss rate or false negative rate
FOR | 0.0 | 0.04348 | 0.0 | False omission rate
FP | 0 | 0 | 1 | False positive/type 1 error/false alarm
FPR | 0.0 | 0.0 | 0.03448 | Fall-out or false positive rate
G | 1.0 | 0.96825 | 0.94868 | G-measure geometric mean of precision and sensitivity
GI | 1.0 | 0.9375 | 0.96552 | Gini index
GM | 1.0 | 0.96825 | 0.98261 | G-mean geometric mean of specificity and sensitivity
HD | 0 | 1 | 1 | Hamming distance
IBA | 1.0 | 0.87891 | 0.99881 | Index of balanced accuracy
ICSI | 1.0 | 0.9375 | 0.9 | Individual classification success index
IS | 1.54749 | 1.24793 | 1.926 | Information score
J | 1.0 | 0.9375 | 0.9 | Jaccard index
LS | 2.92308 | 2.375 | 3.8 | Lift score
MCC | 1.0 | 0.94696 | 0.93218 | Matthews correlation coefficient
MCCI | Very Strong | Very Strong | Very Strong | Matthews correlation coefficient interpretation
MCEN | 0 | 0.125 | 0.1661 | Modified confusion entropy
MK | 1.0 | 0.95652 | 0.9 | Markedness
N | 25 | 22 | 29 | Condition negative
NLR | 0.0 | 0.0625 | 0.0 | Negative likelihood ratio
NLRI | Good | Good | Good | Negative likelihood ratio interpretation
NPV | 1.0 | 0.95652 | 1.0 | Negative predictive value
OC | 1.0 | 1.0 | 1.0 | Overlap coefficient
OOC | 1.0 | 0.96825 | 0.94868 | Otsuka-Ochiai coefficient
OP | 1.0 | 0.94143 | 0.95614 | Optimized precision
P | 13 | 16 | 9 | Condition positive or support
PLR | None | None | 29.0 | Positive likelihood ratio
PLRI | None | None | Good | Positive likelihood ratio interpretation
POP | 38 | 38 | 38 | Population
PPV | 1.0 | 1.0 | 0.9 | Precision or positive predictive value
PR | 0.34211 | 0.42105 | 0.23684 | Positive rate
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 | 0.1662 | 0.06233 | Random accuracy
RACCU | 0.11704 | 0.16638 | 0.0625 | Random accuracy unbiased
TN | 25 | 22 | 28 | True negative/correct rejection
TNR | 1.0 | 1.0 | 0.96552 | Specificity or true negative rate
TON | 25 | 23 | 28 | Test outcome negative
TOP | 13 | 15 | 10 | Test outcome positive
TOPR | 0.34211 | 0.39474 | 0.26316 | Test outcome positive rate
TP | 13 | 15 | 9 | True positive/hit
TPR | 1.0 | 0.9375 | 1.0 | Sensitivity, recall, hit rate, or true positive rate
Y | 1.0 | 0.9375 | 0.96552 | Youden index
dInd | 0.0 | 0.0625 | 0.03448 | Distance index
sInd | 1.0 | 0.95581 | 0.97562 | Similarity index

Generated By PyCM Version 4.6