Classical Supervised Learning
Linear and logistic regression, regularisation, margins, kernels, and the geometry underneath them.
8concepts
156flashcards
60minutes of reading
- 01 Cross-Validation and Model Selection Cross-validation estimates how well a learning procedure generalises, not how well your fitted model will, and the moment you use it to pick among models its estimate of the winner is optimistically biased.
- 02 Naive Bayes and Generative Classifiers Naive Bayes models how each class generates its features under an assumption that is almost always false, and still classifies well, because getting the ranking right is far easier than getting the probabilities right.
- 03 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.