What is a feedback loop in machine learning?
A feedback loop in machine learning is a process where the output of a model is used as input for future predictions, improving accuracy.
Can you give me an example of a feedback loop?
Sure! In recommendation systems, user interactions (like clicks or purchases) are fed back into the model to refine future recommendations.
How does this improve the model's performance?
It allows the model to learn from real user behavior, adapting to changes and improving its predictions over time.
Are there any risks associated with feedback loops?
Yes, if not managed properly, feedback loops can reinforce biases or lead to overfitting, where the model becomes too tailored to past data.
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