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Classical Supervised Learning

Linear and logistic regression, regularisation, margins, kernels, and the geometry underneath them.

4concepts
64flashcards
30minutes of reading
Start with Linear Regression as Projection Drill the deck
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  1. 01 Ridge, Lasso and Elastic Net Why an $\ell_1$ penalty produces exact zeros while $\ell_2$ only shrinks, what each does to correlated features, and why regularisation is a bias-variance trade rather than a way to fix a bad model. intermediate 8m 16 cards