Artificial intelligence · University
Cross-validation, grouped data and time-aware splits · revision notes
A k-fold procedure rotates held-out folds to estimate performance for a specified training process. Splits and all fitted preprocessing must be controlled.
Key ideas and conditions
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.
Explain without looking
- A forecast cutoff is day 50. How many of days 51 through 60 are future evaluation days?
- Why should fold-score variability be reported or considered?
Further reading and review references
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