Lecture
Performance Evaluation: Bootstrap and Performance Metrics
Description
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.
This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.
Watch on MediaspaceOfficial source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.