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Which of the following best describes the purpose of cross-validation?

A) To reduce model complexity during training.B) To optimise training speed by skipping validation steps.C) To evaluate model performance on unseen data while reusing the entire dataset.D) To directly improve AUC scores by splitting data randomly.

When is stratified K-fold cross-validation most appropriate?

A) When training time needs to be minimised.B) When working with imbalanced classification datasets.C) When comparing regression models with continuous targets.D) When the dataset is too large to process in memory.

Which scenario best illustrates a trade-off between model complexity and deployment feasibility?

A) Using a deeper neural network to increase training time.B) Switching from a random forest to a logistic regression for faster prediction on edge devices.C) Increasing the batch size to accelerate training on GPUs.D) Normalising input data to improve model accuracy.

Why might you apply isotonic regression instead of Platt scaling?

A) To increase training accuracy in a linear regression model.B) To fine-tune threshold settings for binary classifiers.C) To correct non-linear miscalibration in predicted probabilities.D) To reduce inference latency during deployment.

Why would you use nested cross-validation?

A) To train deeper models for imbalanced datasets.B) To calibrate model output scores.C) To tune hyperparameters while ensuring unbiased model evaluation.D) To evaluate ROC curves across multiple model types.

Which evaluation method is most appropriate for time-series forecasting?

A) Stratified K-fold cross-validation.B) Leave-one-out cross-validation.C) Time series cross-validation with rolling windows.D) Random shuffling and splitting.

Which of the following is a benefit of using Platt scaling in low-latency applications?

A) It reduces model training time.B) It introduces minimal computational overhead.C) It dramatically increases model interpretability.D) It ensures a perfect calibration curve.

Which strategy would best balance interpretability and recall in a high-stakes healthcare model?

A) Use a deep ensemble to capture rare edge cases.B) Apply a calibrated logistic regression with threshold tuning.C) Use a GAN to synthesise edge-case training data.D) Shift to unsupervised clustering for prediction.

Why might you use a permutation test to compare two models?

A) To speed up cross-validation.B) To validate whether a small performance gain is statistically significant.C) To generate more synthetic training samples.D) To calibrate predicted probabilities.

Which cross-validation strategy is most appropriate for evaluating a model that forecasts future demand based on sequential, time-stamped data?

A) K-fold cross-validationB) Stratified K-fold cross-validationC) Time series cross-validation (walk-forward validation)D) LOOCV (Leave-One-Out Cross-Validation)

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