Probabl shares an explainer on choosing a classifierβs decision threshold
Probabl describes its scikit-learn 1.9 video as a guide to metric_at_thresholds, a new tool it says makes it easier to explore and identify the optimal cutoff for a classifier.
TLDR
Probabl explains that most classifiers output a probability estimateβsuch as β70% likelyββand turning that into a yes/no decision requires choosing a cutoff, or decision threshold. The organization shares a video it describes as a scikit-learn 1.9 walkthrough of metric_at_thresholds, which it says helps users explore and identify the optimal threshold for their use case.
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