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 | 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
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 | 0.1108 | 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
TOPR | 0.34211 | 0.26316 | 0.39474 | Test outcome positive rate
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 4.6