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Module 6 · Concurrency, OS, ML Interview Topics
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ML basics: bias-variance tradeoff
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📝 **Question:** Train 5%, test 35%. What does it mean? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
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📝 **Question:** Train 5%, test 35%. What does it mean? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
A
High bias — both errors are roughly equal once you account for the natural noise floor on this dataset.
B
High variance — overfit. Gap between train and test is the signature. Fixes: more data, regularization, simpler model.
C
Both equal — train and test are within the standard cross-validation tolerance and the model is well calibrated.
D
Bad data — the test split contains label noise the train split doesn't, inflating the apparent generalisation gap.
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