In the context of classification models, Recall, Accuracy, Precision, and F1 Score are metrics used to evaluate the performance of a model. Each metric provides different insights into how well the model is performing. Understanding the differences between these metrics is crucial for choosing the right evaluation criteria based on the specific needs of your project.

Recall

• Definition: Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified by the model.
• Formula: Recall = TP / (TP + FN), where TP is true positives and FN is false negatives.
• Use Case: It is particularly important when the cost of false negatives is high. For instance, in medical diagnosis, missing a positive case (such as failing to identify a disease) could have more severe consequences than falsely identifying the disease (a false positive).

Accuracy

• Definition: Accuracy measures the proportion of true results (both true positives and true negatives) among the total number of cases examined.
• Formula: Accuracy = (TP + TN) / (TP + TN + FP + FN), where TN is true negatives and FP is false positives.
• Use Case: It is a useful metric when the classes in the dataset are well balanced. However, accuracy can be misleading in the presence of imbalanced classes.

Precision

• Definition: Precision measures the proportion of actual positives among the positive cases identified by the model.
• Formula: Precision = TP / (TP + FP).
• Use Case: It is crucial when the cost of false positives is high. For example, in spam detection, a false positive (marking a legitimate email as spam) is usually seen as more problematic than a false negative (failing to detect a spam email).

F1 Score

• Definition: The F1 Score is the harmonic mean of precision and recall. It provides a balance between the precision and recall of your model.
• Formula: F1 = 2 * (Precision * Recall) / (Precision + Recall).
• Use Case: It is useful when you need to take both false positives and false negatives into account. It is particularly helpful in situations where there's an uneven class distribution, as it doesn't inherently favor large numbers of true negatives.

Summary

• Recall is key when missing a positive is costly.
• Accuracy is best when true positives and true negatives are equally important.