Classical Supervised Learning
Linear and logistic regression, regularisation, margins, kernels, and the geometry underneath them.
8concepts
156flashcards
60minutes of reading
- 01 Margins, Kernels and the Support Vector Machine How maximising the distance to the nearest point gives a classifier that depends on a handful of examples, and how the dual formulation lets you work in an infinite-dimensional feature space without ever visiting it.
- 02 Probability Calibration: Platt Scaling and Isotonic Regression A classifier can rank perfectly and still report probabilities that are wrong by a factor of three, and post-hoc calibration fixes the numbers without touching the ranking, at a cost in held-out data that depends on which method you choose.