Explanation of AUC ROC metric




In a recent article in AV the following points were mentioned while discussing AUC-ROC.
It will be appreciated, if these points are further explained with example.

  1. For a model which gives class as output, will be represented as a single point in ROC plot.

  2. Such models cannot be compared with each other as the judgement needs to be taken on a single metric and not using multiple metrics. For instance, model with parameters (0.2,0.8) and model with parameter (0.8,0.2) can be coming out of the same model, hence these metrics should not be directly compared.

  3. In case of probabilistic model, we were fortunate enough to get a single number which was AUC-ROC. But still, we need to look at the entire curve to make conclusive decisions. It is also possible that one model performs better in some region and other performs better in other.