Thresholding is a technique for getting the most value out of a machine learning classifier. Specifically, in classification problems, thresholding refers to setting a threshold for an evaluation score and treating predictions/models differently based on whether they score above the threshold. We try to eliminate underperforming models by employing thresholding strategies.

Accuracy, Precision, and Recall are three of the most popular metrics for evaluating and thresholding machine learning models.

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