What is threshold tuning in AI models?
Threshold tuning is the process of adjusting the decision threshold in classification models to optimize performance.
Why is it important to tune the threshold?
Tuning the threshold helps balance precision and recall, improving the model's effectiveness for specific applications.
Can you give an example of threshold tuning in practice?
Sure! In medical diagnosis, adjusting the threshold can help minimize false negatives, which is crucial for patient safety.
What metrics should I consider when tuning the threshold?
You should consider metrics like accuracy, precision, recall, F1 score, and the ROC curve to evaluate the impact of threshold changes.
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