Junior interview questionAsked at Yahoo · interview write-up
Explain overfitting and underfitting, the bias-variance trade-off, class imbalance and missing data, and how you would detect and handle each.
Model answer
Overfitting: low training error, much higher validation error; add data or regularise. Underfitting: both high; add capacity. That's the bias-variance trade-off. Imbalance: check class counts, use precision-recall metrics, re-weight or resample. Missing data: measure missingness per feature, impute, and add a missing flag.
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