Originating from World War II radar engineering, the receiver operating characteristics (ROC) curve is a common way to compare the effectiveness of ML models against each other. It measures the Recall rate against the fall-out rate (calculated as [1-specificity]) along a threshold measure. The fall-out rate is also known as the False Positive Rate (FPR) or the probability of false alarm.
You may have noticed a trend in that a lot of the measures have several different names. It can get confusing but it is important to know the pseudonyms of the measures since different articles, blogs, and research papers will use different names for them.
In most cases, the ROC curve will be used with binary classification problems. Some examples ...
