Question: Explain the theoretical risk of adding more input features based on error analysis, and describe the practical solution recommended to mitigate this risk.

Sample answer: In theory, adding more input features to an algorithm could increase the model's variance because it increases the complexity of the model. However, in practice, if an increase in variance is actually observed after adding features inspired by error analysis, the recommended solution is to apply regularization. Regularization will usually eliminate this increase in variance, allowing the new features to successfully help with both bias and variance without causing overfitting.

Key points:

• Adding more features theoretically increases variance.
• Regularization is the recommended practical solution.
• Regularization usually eliminates the increase in variance.

Rubric: A strong response will identify increased variance as the theoretical risk and explicitly name regularization as the practical solution to eliminate that risk.