Case context: A machine learning engineer performs error analysis on a speech recognition system and notices it frequently fails to transcribe audio with background traffic noise. To eliminate this particular category of errors, the engineer creates additional input features specifically designed to filter out traffic frequencies. While the avoidable bias decreases significantly, the engineer notices the model's variance has now increased.

Question: Based on the principles for reducing avoidable bias, what specific technique should the engineer decide to use next to handle the newly introduced variance?

Sample answer: The engineer should use regularization, which will usually eliminate the increase in variance caused by the addition of the new traffic-filtering features.

Key points:

• The engineer successfully added features based on error analysis to reduce bias.
• The theoretical consequence of increased variance occurred.
• Regularization is the correct technique to eliminate the variance increase.

Rubric: The answer must correctly identify regularization as the required intervention to fix the variance increase.