Explain the bias–variance trade-off

Describe the meaning of the bias–variance trade-off in supervised learning.

Your answer should address:

• The definitions of bias and variance in the context of a prediction model.
• How total expected error can be broken down into bias, variance, and irreducible noise.
• The effect of model complexity on bias and variance, including underfitting versus overfitting.
• How you would apply this idea in practice when selecting or tuning models.

Overview: This question tests knowledge of the bias–variance trade-off in supervised learning, including the definitions of bias and variance, the decomposition of error into bias, variance, and irreducible noise, and how model complexity relates to underfitting and overfitting; it falls within the Machine Learning domain.