The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel

AUC is a performance metric for classification tasks that overcomes the limitations of a single confusion matrix by evaluating a model across all thresholds. The metric is the area under the ROC (Receiver Operating Characteristic) curve, which plots the True Positive Rate (TPR) against the False Positive Rate (FPR). This curve is constructed by sorting model scores and calculating the TPR/FPR pair at every possible threshold, revealing the model's performance independent of a specific cutoff. The resulting AUC value represents the probability that the model will rank a randomly chosen positive example higher than a randomly chosen negative one, making it a measure of ranking quality.