Evaluation Protocols

This lecture covers the evaluation protocols in machine learning, focusing on recall, precision, accuracy, F-measure, and specificity. It discusses the trade-offs between recall and precision, the importance of specificity in testing, and real-world examples like COVID-19 testing methods and cancer screening. The instructor explains the concepts using examples such as perfect recall and precision scenarios, strategies to improve precision, and the F1-score as a harmonic mean of recall and precision. Additionally, it explores the Receiver-Operator Curve (ROC) and decision thresholds in binary classification.