In case of a machine learning model, there are four key metrics used to assess different aspects of its performance: Accuracy, Precision, Recall, and F1-Score.
As an example, let's consider a machine learning model for epilepsy detection.
Accuracy
Accuracy is the ratio of correctly predicted samples to the total number of predictions. This includes both correctly identified positive and negative cases. In the context of epilepsy detection, accuracy answers the question: "How many patients (both ill and healthy) were correctly diagnosed?"
Precision
Precision is the proportion of true positive predictions out of all positive predictions made by the model. In the case of epilepsy detection, precision measures how many of the patients diagnosed as epileptic were actually epileptic.
Recall
Recall refers to the proportion of true positive predictions among all actual positive cases. In epilepsy detection, recall answers the question: "Out of all patients who truly have epilepsy, how many were correctly identified by the model?"
F1-Score
The F1-score combines Precision and Recall. It allows for an easy comparison of models by summarizing both Precision and Recall into a single value.
