Statistics AUC and ROC curve - Code World

Statistics AUC and ROC curve

Others 2020-02-24 12:56:54 views: null

In class prediction algorithm, we tend to have the following four cases:

1. correctly predicted to be wrong (FPR, false negative rate)

2. correctly predicted to be correct (TPR, true positive rate)

3. predicted to correct the error (FNR, false negative rate)

4. prediction error into an error (TNR, true negative rate)

We are more concerned about the TPR and FPR of them, each calculation can be calculated from these two values. If we have to prediction algorithm is a binary classification algorithm, then we can get a group of (TPR, FPR) of sequence by adjusting the threshold classification, this sequence of curves in TPR-FPR image formed is the ROC curve, curve and the coordinate axes is the area enclosed by AUC (area under the curve)

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Origin www.cnblogs.com/Aurora-Borealis/p/12356246.html

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