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How do you select a threshold value in classification problem

 Choosing the Threshold:

  • There is no one-size-fits-all threshold. The choice depends on your specific use case and business requirements.

  • You can visualize the trade-off between precision and recall by plotting a Precision-Recall Curve (PRC) for various threshold values. Then, you can select the threshold that best suits your needs based on the curve and the associated trade-off.

  • If you have a clear understanding of the costs and benefits of false positives and false negatives in your application, it can guide your choice of threshold.

Here's a simplified guideline:

  1. 1. If false positives are more costly (e.g., medical diagnoses), increase the threshold to prioritize precision.

  3. 2. If false negatives are more costly (e.g., spam detection), decrease the threshold to prioritize recall.

  5. 3. In cases where you need a balance, you might consider an F1-score optimized threshold, which balances precision and recall.
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