Lecture
This lecture covers the Receiver Operating Characteristic (ROC) curve, focusing on concepts like True Positive Rate, False Positive Rate, Area Under the Curve (AUC), and the interpretation of prediction probabilities. The instructor explains the importance of the ROC curve in evaluating classification models and demonstrates how to calculate sensitivity, specificity, and accuracy. Additionally, the lecture delves into the distribution of prediction probabilities and the impact of changing the threshold on the ROC curve. Practical examples using Python's predict_proba() function and the AUC ROC curve are provided.