ISTQB Certified Tester: AI Testing Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose B0

  - K-fold cross-validation partitions the data into k folds and trains/evaluates k times, each time using a different fold as the validation set. Averaging the k results produces a more stable, lower-variance performance estimate, particularly valuable when data is limited. It does not reduce training time (it actually increases compute), does not enlarge the dataset, and does not by itself fix class imbalance.