*3.3. Performance Measures*

### 3.3.1. Cross-Fold Validation

The k-fold cross-validation technique is extensively applied to confirm the accuracy of algorithms, as it reduces biases that are associated with randomly sampling training and test sets. Kohavi (1995) verified that ten-fold cross-validation was optimal [71]; it involves dividing a complete dataset into ten subsets (nine learning subsets and one test subset).
