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What are training, validation, and test splits? Describe a typical split strategy for a dataset of 100k examples and explain how you would modify splits if data is time-series or suffers from class imbalance.

Hints

!1. Standard random splits vs time-aware splits!<

!2. Use stratified sampling for class imbalance!<

Sample Answer

Training/validation/test splits: training fits model, validation tunes hyperparameters, test estimates final generalization. For 100k examples: common split 70/15/15 (70k train, 15k val, 15k test). If time-series: use chronological splits (e.g., first 80% train, next 10% val, final 10% test) to avoid leakage and mimic production. For class imbalance: ensure stratified splits so class proportions are preserved across sets; if minority class is tiny, consider oversampling/SMOTE on training only or use larger validation/test sets for reliable estimates. Also use cross-validation or repeated stratified folds when data is limited.

Follow-up Questions to Expect

1. When would you use cross-validation instead of a fixed validation set?

2. How would you detect leakage between splits?