ROC AUC - Area under the ROC curve

The area under the ROC curve (ROC AUC) is a way to measure how well a machine learning model can predict the right answer, just like in the coin-tossing game where you have to guess if a coin is heads or tails. The ROC curve plots the proportion of times you correctly guessed heads (also called the “true positive rate”) against the proportion of times you incorrectly guessed heads (also called the “false positive rate”).

The area under this curve is a measure of how well you did at guessing. If the AUC is close to 1, it means you did a good job at guessing. If the AUC is close to 0, it means you didn’t do a very good job.

The probabilistic meaning of the ROC AUC is that it represents the probability that a classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. A classifier with an AUC of 1 will rank all positive examples higher than all negative examples, while a classifier with an AUC of 0.5 will rank examples randomly.