-fold cross-validation
Definition taught in Estimating error rates, part of Statistical learning
-fold cross-validation
| Type | Definition |
| Taught in | Estimating error rates |
| Part | Statistical learning |
\widehat{\mathrm{Err}}_{\mathrm{KCV}} =\frac1n\sum_{k=1}^{K}\sum_{i=1}^{n_k} Q\bigl(y_i^{(k)},\eta_{(X^{(-k)},y^{(-k)})}(x_i^{(k)})\bigr).
Here n_k is the size of fold k, and (-k) denotes the fit that excludes that fold. Pooling individual errors gives each observation equal weight even if the fold sizes differ.
Used in
- Taught in Estimating error rates, where the derivation and examples are.
- All 7 questions on Estimating error rates
See also
Cross-validation · Leave-one-out cross-validation · Nested cross-validation · Cross-validation of the likelihood · Fixed-rule error · Confusion matrix · Apparent error · Bootstrap error and the correction