A model that predicts everyone is healthy might have high accuracy, but it's a failure in a hospital. Evaluation metrics reveal the true character of your model.
1The Confusion Matrix
The Confusion Matrix is a 2x2 grid that summarizes the predictive performance of a classification model. It categorizes every prediction into one of four buckets: True Positive (Correct hit), True Negative (Correct rejection), False Positive (Type I Error), and False Negative (Type II Error).
2Precision vs. Recall
There is often a tradeoff between Precision (Quality) and Recall (Quantity). Precision measures how trustworthy your positive predictions are, while Recall measures how complete your positive identification is. In medicine, we often sacrifice Precision to ensure high Recall.
3The F1 Balance
When you can't decide which to prioritize, the F1-Score offers a middle ground. By calculating the harmonic mean of Precision and Recall, it penalizes extreme values, ensuring that a model must perform reasonably well in both areas to achieve a high score.
