Question: Differentiate between bias and variance in the context of machine learning models.

Answer: Bias refers to the error introduced by approximating a real-world problem, and variance refers to the model's sensitivity to fluctuations in the training data. Balancing bias and variance is crucial for model performance.

Example:

A linear regression model might have high bias if it oversimplifies a complex problem, while a high-degree polynomial may have high variance.