Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Lecture
Variable Selection Methods
In course
DEMO: incididunt quis
Amet quis cupidatat aliquip duis excepteur excepteur mollit mollit duis eiusmod qui. Veniam ad sunt deserunt ea do veniam sint nulla esse proident aliquip occaecat. Proident nulla mollit irure quis reprehenderit do. Magna cupidatat ad sint sint aute velit quis sint ut. Dolor Lorem adipisicing voluptate reprehenderit quis enim occaecat eiusmod amet. Nisi officia est deserunt labore ullamco deserunt. Eu in nostrud velit tempor dolore.
Description
This lecture covers various methods for variable selection in statistical modeling, including automatic selection algorithms, information criteria, and prediction error analysis. It discusses the trade-off between bias and variance, model diagnostics, and the Akaike information criterion. The instructor emphasizes the importance of selecting the right model complexity to improve prediction accuracy.
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.