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    Probabl shares an explainer on choosing a classifier’s 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.

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    2 Sources, 23d ago, first seen 23d ago

    TLDR

    Probabl explains that most classifiers output a probability estimate—such as “70% likely”—and 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.

    Combined views

    541

    2 Sources, first seen 23d ago

    Combined views

    541

    2 Sources, first seen 23d ago

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    5 saves
    2 reposts

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    2 Sources

    @probabl_aiIn our latest @scikit_learn version 1.9 explainer video, contributor Emily Chen walks you through metric_at_thresholds – a handy new tool that makes it easier than ever to explore and identify the optimal decision threshold for your next classifier 🔬 https://www.youtube.com/shorts/DVpRCCy0KSk
    @GaelVaroquauxRT @probabl_ai: In our latest @scikit_learn version 1.9 explainer video, contributor Emily Chen walks you through metric_at_thresholds – a…

    2 Sources

    @probabl_aiIn our latest @scikit_learn version 1.9 explainer video, contributor Emily Chen walks you through metric_at_thresholds – a handy new tool that makes it easier than ever to explore and identify the optimal decision threshold for your next classifier 🔬 https://www.youtube.com/shorts/DVpRCCy0KSk
    @GaelVaroquauxRT @probabl_ai: In our latest @scikit_learn version 1.9 explainer video, contributor Emily Chen walks you through metric_at_thresholds – a…