A score becomes a decision only after you choose a threshold
ROC-AUC evaluates how well a classifier ranks positive cases above negative cases across every possible threshold. It does not select the operating threshold for you.
The selected threshold creates the confusion matrix. Moving it changes false positives and false negatives, so the right operating point depends on the cost of each error in the real decision context.
Run, inspect, and compare
Follow the three guided moves above. Change one variable at a time so every visual change has a clear cause.
By the end of this lesson, you will be able to:
- Calculate and interpret accuracy, precision, recall, specificity, and F1 from a confusion matrix.
- Explain how changing the decision threshold alters classification outcomes and operating trade-offs.
- Interpret a ROC curve and distinguish threshold-independent AUC from one selected operating point.
In the previous lessons, we built a neural network, trained it on real data, updated weights using gradients, and selected an optimizer. Now it is time to ask a critical question: how do we know if our model is actually good? The goal of machine learning is not to memorize training data, but to generalize - that is, to perform well on new, unseen data. This ability is known as the model's generalization ability, and it is the central measure of any predictive system.