Explain the bias–variance trade-off

Q: Explain the bias–variance trade-off

This question evaluates understanding of the bias–variance trade-off in supervised learning, covering definitions of bias and variance, error decomposition into bias, variance, and irreducible noise, and the relation between model complexity, underfitting, and overfitting, and it belongs to the Machine Learning domain.

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Question

Explain the bias–variance trade-off in supervised learning.

In your answer, cover:

What bias and variance mean in the context of a prediction model.
How total expected error can be decomposed into bias, variance, and irreducible noise.
How model complexity affects bias and variance (underfitting vs. overfitting).
How you would use this concept in practice when choosing or tuning models.

Explain the bias–variance trade-off

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Explain the bias–variance trade-off

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