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  • Performance Metrics for Binary Classification Problems Cheatsheet

    This article is merely for a quick recap of Machine Learning Knowledge, which can not be served as a tutorial.
    All rights reserved by Diane(Qingyun Hu).

    Prerequisites

    TP: True Positive
    FP: False Positive
    TN: True Negative
    FN: False Negative

    Recall

    = Sensitivity = TPR(True Positive Rate)
    egin{equation}
    Recall = frac{TP} {TP + FN}
    end{equation}

    Precision

    egin{equation}
    Precision = frac{TP} {TP + FP}
    end{equation}

    Accuracy

    egin{equation}
    Accuracy = frac{TP + TN} {TP + FP +TN + FN}
    end{equation}

    F1 Score

    egin{equation}
    F1 Score = frac{2 * Recall * Precision} {Recall + Precision}
    end{equation}

    Specificity

    egin{equation}
    Specificity = frac{TN} {TN + FP}
    end{equation}

    FPR(False Positive Rate)

    = 1 - Specificity
    egin{equation}
    FPR = frac{FP} {TN + FP}
    end{equation}

    ROC Curve

    x-axis: FPR ( = 1 - Specificity )
    y-axis: TPR ( = Recall )

    AUC (Area under the ROC Curve)

    The bigger the size of AUC is, the better.

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  • 原文地址:https://www.cnblogs.com/DianeSoHungry/p/11288143.html
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