Update README.md

README.md

@@ -64,4 +64,8 @@ The model is a feedforward neural network designed for a binary classification t
 | :--- | :--- |
 | **Validation AUC** | 0.9790 |
 | **Validation Avg. Precision**| 0.638 |
-| **Validation Top-1 Accuracy**| 50.0% |
+| **Validation Top-1 Accuracy**| 76.0% |
+
+--> AUC measures how well the model separates "better vs. worse" items across all possible score thresholds. E.g., if you randomly take one "positive" candidate and one "negative", the model puts a higher score on the positive 97.9% of the time.
+--> Average Precision is the area under the Precision-Recall curve. It emphasizes how pure the top of the ranking is as you move down the list of candidates. This value means that as you sweep through candidates from best to worse, the precisioned maintained on "positive" scores averages to 0.64.
+--> Top-1 Accuracy is the fraction where the model's #1 choice equals the true best among cases where there is a single ground truth best.