Linear Models for Classification: Recap and Binary Classification
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Description
This lecture covers a recap of linear models in dimension D, explaining how to write them and their application in binary classification as regression, illustrated with a tumor classification example.
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Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
