Lecture
Model Evaluation
Description
This lecture covers model evaluation in machine learning, focusing on model assessment and selection, theory behind expected loss, training error, and prediction error. It also discusses resampling methods like cross-validation and bootstrapping, as well as information criteria such as AIC and BIC. The instructor emphasizes the importance of comparing model performance to baseline models and quantifying uncertainty in performance metrics.
Official 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.