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Given a vector of scores and a vector of actual class labels, how do you calculate a single-number AUC metric for a binary classifier in the R language or in simple English?
Page 9 of "AUC: a Better Measure..." seems to require knowing the class labels, and here is an example in MATLAB where I don't understand
R(Actual == 1)) Because R (not to be confused with the R language) is defined a vector but used as a function?
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The roc() function takes the actual and predicted value as an argument and returns a ROC curve object as result. Then, to find the AUC (Area under Curve) of that curve, we use the auc() function. The auc() function takes the roc object as an argument and returns the area under the curve of that roc curve.
The AUC can be defined as the probability that the fit model will score a randomly drawn positive sample higher than a randomly drawn negative sample. This is also equal to the value of the Wilcoxon-Mann-Whitney statistic. This function is a wrapper for functions from the ROCR package.
With the package pROC you can use the function auc() like this example from the help page:
> data(aSAH) > > # Syntax (response, predictor): > auc(aSAH$outcome, aSAH$s100b) Area under the curve: 0.7314 The ROCR package will calculate the AUC among other statistics:
auc.tmp <- performance(pred,"auc"); auc <- as.numeric([email protected]) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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