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    Choosing a classifier’s yes/no cutoff: A post asks what fits your use case

    Most classifiers output probability estimates, such as “70% likely,” a post explains. Turning those estimates into yes/no decisions means choosing a decision threshold.

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

    TLDR

    A post asks how to choose the right decision threshold—the cutoff used to turn a classifier’s probability estimate into a yes/no decision. Its central question is how to know which threshold fits a particular use case.

    Combined views

    1.3K

    2 Sources, first seen 23d ago

    5 likes

    Combined views

    1.3K

    2 Sources, first seen 23d ago

    5 likes
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    3 reposts

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    1 comments
    4 saves
    3 reposts

    Sentiment

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    2 Sources

    @probabl_aiMost classifiers output a probability estimate (e.g. 70% likely), and turning that estimate into a yes/no decision means picking a decision threshold. But how do you know what is the right threshold for your use case? 🤔
    @GaelVaroquauxRT @probabl_ai: Most classifiers output a probability estimate (e.g. 70% likely), and turning that estimate into a yes/no decision means pi…

    2 Sources

    @probabl_aiMost classifiers output a probability estimate (e.g. 70% likely), and turning that estimate into a yes/no decision means picking a decision threshold. But how do you know what is the right threshold for your use case? 🤔
    @GaelVaroquauxRT @probabl_ai: Most classifiers output a probability estimate (e.g. 70% likely), and turning that estimate into a yes/no decision means pi…