Bayes-optimal rule
Theorem taught in Statistical classifiers and discriminant analysis, part of Statistical learning
Bayes-optimal rule
| Type | Theorem |
| Taught in | Statistical classifiers and discriminant analysis |
| Part | Statistical learning |
A rule that minimises the conditional risk for every \mathbf{x} is a Bayes rule, also called a Bayes-optimal or minimum-risk rule. Under zero–one loss it is
\eta(\mathbf{x})=B(\mathbf{x})=\arg\max_{g\in\{1,\ldots,G\}}\tau_g(\mathbf{x}).
Used in
- Taught in Statistical classifiers and discriminant analysis, where the derivation and examples are.
- All 8 questions on Statistical classifiers and discriminant analysis
See also
Bayes action for two hypotheses · Bayes classifier · Zero–one loss and risk · Training data and classifier · Posterior class probability · Fisher’s direction · Classification problem · Error rates and optimal classifiers