Training data and classifier
Definition taught in Statistical classifiers and discriminant analysis, part of Statistical learning
Training data and classifier
| Type | Definition |
| Taught in | Statistical classifiers and discriminant analysis |
| Part | Statistical learning |
The training data consist of multivariate observations \mathbf{x}_1,\ldots,\mathbf{x}_n\in\mathbb{R}^p of p predictor variables, stored as the rows of a data matrix X_{\mathrm{all}}, together with class labels \mathbf{y}_{\mathrm{all}}=(y_1,\ldots,y_n)^\top, y_i\in\{1,\ldots,G\}, where G is the number of classes. A classifier is a function
\eta^{(X_{\mathrm{all}},\mathbf{y}_{\mathrm{all}})}(\mathbf{x})\in\{1,\ldots,G\}
of a new observation \mathbf{X}=\mathbf{x} and the training set.
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
Training set · Posterior class probability · Zero–one loss and risk · Bayes-optimal rule · Fisher’s direction · Classification problem · Bayes classifier · Error rates and optimal classifiers