# Cross-validation, grouped data and time-aware splits

> A k-fold procedure rotates held-out folds to estimate performance for a specified training process. Splits and all fitted preprocessing must be controlled.

Course: [Machine learning methods and validation](https://studybrix.com/en/courses/machine-learning-methods/index.md)
Level: university
Prerequisites: Training/validation concepts and averages.

## Notes

### Cross-validation

A k-fold procedure rotates held-out folds to estimate performance for a specified training process. Splits and all fitted preprocessing must be controlled.

### Grouped records

When multiple records share a person, device or source, the split should match whether deployment encounters new or known groups.

### Time series

Forecast evaluation must respect time order. Training on future information while predicting earlier records creates a misleading task.

### Nested choices

Using the same folds for extensive tuning and final performance reporting can produce optimistic estimates. Separate outer evaluation or a final untouched test can help.

## Practice questions

### 1. A 100-record dataset is divided equally into 5 folds. How many records are in one held-out fold?

Answer: 20

Explanation: 100/5=20.

### 2. Three fold scores are 0.7,0.8,0.9. Find their arithmetic mean.

Answer: 0.8

Explanation: (0.7+0.8+0.9)/3=0.8.

### 3. Which split is suitable when evaluating prediction on new devices?

A. Keep records from each device within a single side of a split
B. Randomly mix near-duplicate device records everywhere
C. Always put labels into inputs
D. Split only by filename length

Answer: Keep records from each device within a single side of a split

Explanation: Group-aware splits avoid sharing device-specific signals across sides.

### 4. For a genuine forecasting task, what should training data generally precede?

A. No date restriction ever
B. The alphabet's last letter
C. The forecast evaluation time
D. Only the output filename

Answer: The forecast evaluation time

Explanation: Use past information to predict later outcomes.

### 5. Why must imputation or scaling be fitted inside each training fold?

Sample answer / marking guidance: Otherwise held-out data influence fitted preprocessing, producing leakage and an overly favourable assessment.

Explanation: Credit preprocessing as part of the learned procedure.

### 6. What does nested cross-validation separate?

A. Hyperparameter selection from outer evaluation
B. Model names from file names
C. All data from every label permanently
D. Every numeric feature from computation

Answer: Hyperparameter selection from outer evaluation

Explanation: Inner selection and outer evaluation serve different roles.

### 7. A forecast cutoff is day 50. How many of days 51 through 60 are future evaluation days?

Answer: 10

Explanation: Count the ten subsequent days inclusively.

### 8. Why should fold-score variability be reported or considered?

Sample answer / marking guidance: It can reveal sensitivity to splits, small samples or heterogeneity. A single mean hides that uncertainty and possible instability.

Explanation: Credit variability and its interpretation.

## Further reading

- https://developers.google.com/machine-learning/crash-course
- https://cs229.stanford.edu/

Original AI-assisted educational draft; not independently teacher-reviewed. References support further study, not copied exercises, affiliation or curriculum accreditation.