Wikis
Info-nuggets to help anyone understand various concepts of MLOps, their significance, and how they are managed throughout the ML lifecycle.
Model threshold
Thresholding is a technique for getting the most value out of a machine learning classifier.
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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