Beyond the Formulas: A Practical Framework for Choosing Precision vs. Recall

Author(s): Ashish Johnson

Originally published on Towards AI.

Beyond the Formulas: A Practical Framework for Choosing Precision vs. Recall

You studied about Precision and Recall, but still struggling to figure out the right one for your business problem?

The article explains the importance of choosing the right metric between precision and recall in business problem-solving. It introduces a “Cost of Error” framework that helps connect abstract metrics to real-world consequences, emphasizing the significance of evaluating errors in a model’s predictions. The author outlines a four-step process for implementing this framework, followed by real-world scenarios across various industries—such as healthcare, fintech, and e-commerce—demonstrating how to apply these concepts effectively. The takeaway stresses that the choices between precision and recall are fundamentally business decisions based on risk and consequence assessment.

Read the full blog for free on Medium.

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Published via Towards AI