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Learn Bias Variance Data Science Tutorial, validate concepts with Bias Variance Data Science MCQ Questions, and prepare interviews through Bias Variance Data Science Interview Questions and Answers.
Bias-Variance Q&A
1What is bias?
Answer: Error from overly simplistic assumptions in a model.
2What is variance?
Answer: Error from model sensitivity to training data fluctuations.
3Bias-variance tradeoff?
Answer: Balance underfitting and overfitting for best generalization.
4High bias symptoms?
Answer: Low train accuracy and low test accuracy (underfit).
5High variance symptoms?
Answer: High train accuracy but poor test accuracy (overfit).
6How reduce bias?
Answer: Use richer features/model capacity or reduce regularization.
7How reduce variance?
Answer: More data, regularization, pruning, ensembling, or simpler model.
8Role of cross-validation?
Answer: Estimates model stability and helps choose complexity.
9Effect of noisy labels?
Answer: Usually increases variance and hurts model reliability.
10How regularization helps?
Answer: It lowers variance by penalizing complexity.
11Can ensemble reduce variance?
Answer: Yes, bagging/averaging stabilizes predictions.
12One-line summary?
Answer: Best models strike the right bias-variance balance.