Lecture

Error Decomposition and Regression Methods

Description

This lecture covers error decomposition in regression, including reducible and irreducible errors, polynomial regression for flexible modeling, and K Nearest-Neighbors for non-linear predictions. It also discusses underfitting, overfitting, and the bias-variance trade-off.

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Official source

https://mediaspace.epfl.ch/media/0_eems7skn

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Ontological neighbourhood

Statistics

Data analysis: Regression analysis, Cross-validation, Statistical classification

Statistical inference: Mathematical statistics

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