How do feedback loops affect ML models?

What are feedback loops in machine learning?

Feedback loops in machine learning occur when the output of a model influences its future inputs, creating a cycle.

How do these feedback loops impact model performance?

They can enhance performance by allowing models to adapt to new data, but they can also lead to overfitting if not managed.

Can you give an example of a feedback loop in action?

Sure! In recommendation systems, user interactions with suggested items can refine future recommendations, creating a feedback loop.

What are the risks of feedback loops?

Risks include reinforcing biases, leading to poor decision-making, and creating a lack of diversity in outputs.