Precision-recall plots with curves for each class can be plotted, as seen here. But I don't quite understand how this is done. For a single PR curve, the ratio of correct to incorrect positive and negative classifications determines the curve. How is this done for each class?

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    $\begingroup$ Assuming you are dealing with binary data, you would simply repeat the procedure by considering each class as the positive class and the other as negative class. This can be easily extended to multi-class scenario. $\endgroup$ – DataD'oh Aug 16 '17 at 9:13

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