What are feedback loops and how do they affect ML performance?

What are feedback loops and how do they affect ML performance?

Feedback loops in machine learning refer to the process where the output of a model is used as input for future predictions. This can significantly enhance performance by allowing the model to learn from its mistakes and successes.

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

Sure! A common example is in recommendation systems. When a user interacts with recommended items, that data is fed back into the system to refine future recommendations, improving accuracy over time.

What are the potential downsides of feedback loops?

One downside is that they can reinforce biases present in the training data, leading to a model that perpetuates existing issues rather than correcting them.

How can we mitigate the negative effects of feedback loops?

To mitigate negative effects, it's important to regularly audit the data, incorporate diverse datasets, and implement mechanisms to detect and correct biases.