From: Application of machine learning in ophthalmic imaging modalities
Evaluation metrics | Definitions |
---|---|
Accuracy | The proportion of both positives and negatives that are correctly identified; the higher the accuracy, the better the classifier |
Sensitivity/Recall | The proportion of positives that are correctly identified |
Specificity | The proportion of negatives that are correctly identified |
Precision | The proportion of positives that are correctly identified among all positive identified samples |
Kappa value | To show the actual agreement between two sets of observations |
Dice coefficient/F1 score | Harmonic average of the precision and recall, where an F1 score reaches its best value at 1 and worst at 0 |