Precision, Recall, F1 Score and ROC AUC Explained

These metrics go beyond simple accuracy and give a deeper picture of how a classification model performs, especially on imbalanced data.

Precision measures how many predicted positives were actually correct.

Precision = True Positives / (True Positives + False Positives)

Recall measures how many actual positives the model correctly identified.

Recall = True Positives / (True Positives + False Negatives)

F1 Score combines precision and recall into a single number, useful when you need both to be reasonably high.

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

ROC AUC measures how well a model separates the two classes across different threshold values. A score closer to 1 means the model distinguishes classes almost perfectly, while a score near 0.5 means it performs no better than random guessing.