Question: When is it particularly important to prioritize precision as an evaluation metric for a Machine Learning model? When the cost of false positives is high,

When is it particularly important to prioritize precision as an evaluation metric for a Machine Learning model? When the cost of false positives is high, such as in medical diagnoses When dealing with large datasets to reduce computation time When the model's training speed is a priority When aiming to increase the model's generalizability across different datasets

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