difficulty 4/5
Gaussian discriminant analysis
A classifier that models what each class's data looks like — fitting a Gaussian per class — and then uses Bayes' rule to turn that into a prediction.
Intuition
There are two ways to build a classifier, and they are genuinely different in kind.
Discriminative models — logistic regression, SVM — learn the boundary directly. They answer "given these features, which class?" and never model the features themselves.
Generative models ask the other question first: what does each class's data look like? Fit a description of class A, fit a description of class B, then for a new point ask which description fits it better. Gaussian discriminant analysis is the version where each description is a Gaussian.
Formal definition
How it works
Where it's used
Why & when
Worked example
Pitfalls
Check yourself
3 questions
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Sources
- scikit-learn User Guide — Linear and Quadratic Discriminant Analysisdocs · retrieved 2026-08-31https://scikit-learn.org/stable/modules/lda_qda.html
- On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes (2001)paper · retrieved 2026-10-07https://proceedings.neurips.cc/paper/2001/hash/7b7a53e239400a13bd6be6c91c4f6c4e-Abstract.html