How can we reduce false negatives in AI models?
An example of an AI chatbot conversation about reducing false negatives in AI models.
What are false negatives in AI models?
False negatives occur when a model incorrectly predicts a negative outcome for a positive instance.
Why is it important to reduce false negatives?
Reducing false negatives is crucial as it can lead to missed opportunities or critical errors, especially in fields like healthcare.
What techniques can be used to reduce false negatives?
Techniques include improving data quality, using ensemble methods, and adjusting classification thresholds.
Can you give an example of a real-world application?
Sure! In medical diagnosis, reducing false negatives can ensure that diseases are not overlooked, leading to better patient outcomes.
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