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The AUC is a summary statistic of your dataset. As such, if you did the same experiment again you would get a slightly different value - and if repeated several times, you'd get a distribution of values. You probably want to show that your distribution rarely or never includes 0.5 (indicating random classification). When you have an empirical distribution ...


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I don't think there is any reason to modify the matrix so keep it as it is. Even if you scale it what purpose does it serve? At the end of the day your model does not change even if you modify your confusion matrix. In my opinion you can use other metrics e.g. f1-score (or f beta score), AUC score, etc to judge your model. Confusion matrix only provides ...


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