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.
| Actual | Predict 
 | 
| 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 | 
| 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 | 
| 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 4.3