Performance Evaluation: Bootstrap and Performance Metrics

This lecture covers the evaluation of machine learning models through techniques like leave-one-out and bootstrap, discussing their advantages and disadvantages. It also explains performance metrics such as confusion matrices, recall, precision, and F-measure. The instructor provides examples to illustrate these concepts, including scenarios related to medical diagnostics. Terminologies like false positives and false negatives are defined, and the importance of recall in model evaluation is emphasized.