Loading...
Loading...
Browse 3 real-world technical and behavioral interview questions about Cross validation. Review scenarios, edge cases, and architectural best practices.
Build the split to imitate the gap between training and deployment. Group all of an entity's rows onto one side when rows repeat per entity, split forward in time for anything temporal with an embargo covering the label window, and treat a plain random shuffle as the default that silently inflates almost every score.
Detect overfitting by comparing training and validation performance on an honest split. Fix it with more or better data, simpler models, regularisation, early stopping and a final test set that is read only once. It also connects model validation to the point an interviewer is testing.
A random train-test split ruins a forecasting model by leaking future observations into training. Use time-based holdouts or rolling-origin backtesting, and build every feature only from data available before the forecast timestamp. Use this time series answer to show the decision, trade-off, and evidence rather than a memorised definition.