�
Metrics that built around confusion matrix:
- [X] Confusion Matrix (True Positive, True Negative, False Positive - Type I Error, False Negative - Type II Error)
- [X] True Positive Rate (TPR) / Hit Rate / Recall / Sensitivity / Detection Rate
- [X] True Negative Rate (TNR) / Specificity / Selectivity
- [X] False Positive Rate (FPR) / Fall-out / False Alarm Rate (FAR)
- [X] False Negative Rate (FNR) / Miss Rate
- [X] Accuracy / Correct Classification Rate (Correctly Classified Rate)
- [X] Misclassification Rate
- [X] Balanced Accuracy
- [ ] Balanced Error Rate
- [X] Positive Predicted Value (PPV) / Precision
- [ ] Average Precision
- [X] False Discovery Rate (FDR)
- [X] False Omission Rate (FOR)
- [ ] Positive Likelihood
- [ ] Negative Likelihood
- [X] Prevalence
- [X] F1 Score
- [X] Matthews Correlation Coefficient (MCC)
- [ ] Discriminant Power
- [X] Informedness (Bookmaker Informedness - BM) / Youden Index (Youden’s J Statistic)
- [X] Markedness (MK)
- [X] AUROC (Area Under ROC)
- [X] AR (Accuracy Ratio) / Gini Coefficient
- [X] KS Statistic
- [ ] AUPR (Area Under Precision-Recall Curve)
- [ ] Bayesian Error Rate
- [X] Fowlkes-Mallows Index / G-Score
- [X] Cohen Kappa
- [ ] Hamming Loss
- [ ] Hinge Loss
- [ ] Jaccard Score
Proper scoring rule:
- [X] Log Loss & Mean Log Loss
- [X] Brier Score
- [X] Explained Variance
- [X] Max Error
- [X] Mean Absolute Error
- [X] Mean Squared Error
- [ ] Normalized Mean Squared Error
- [X] Root Mean Squared Error
- [X] Mean Squared Logarithmic Error
- [X] Median Absolute Error
- [ ] Mean Absolute Percentage Error
- [ ] Mean Absolute Scaled Error
- [ ] Median Squared Error
- [X] R2 Score
- [ ] Adjusted R2 Score
- [ ] M-Estimators
- [ ] Adjusted Mututal Information Score / Mutual Information Score
- [ ] Adjusted Rand Score
- [ ] Calinski-Harabasz Score
- [ ] Davies-Bouldin Score
- [ ] Completeness Metric
- [ ] V-Measure Score
- [ ] Homogeneity Score
- [ ] Mean Silhouette Coefficient / Silhouette Coefficient