##### What is AUC ?

AUC is the area under the ROC curve. It is a popularly used classification metric.

Classifiers such as logistic regression and naive bayes predict class probabilities as the outcome instead of the predicting the labels themselves. A new data point is classified as positive if the predicted probability of positive class is greater a **threshold**. Each threshold leads to a different classifier. Hence, typical metrics such as accuracy and F1 score depend on the threshold one picks. AUC for such classifiers gives an aggregated metric across thresholds.

### Why do we care about AUC and the ROC curve?

AUC is popular because

- It is a threshold independant metric – Helps evaluate the model without being dependent on the specific threshold we choose
- The ROC curve is often used to chose the threshold

*Some classifiers such as an SVM or a perceptron give the class labels directly as the outcome and not class probabilities. ***Does is make sense to compute the AUC metric for classifiers such as the SVM which give class labels as outcome? **

The answer is Yes. It is often useful to get class probability outcomes instead of absolute class values.

### Platt Scaling: How to Compute AUC for an SVM Classifier ?

The following video explains computing the AUC metric for an SVM classifier, or other classifiers that give the absolute class values as outcomes.

The video explains the process of *calibrating* the outcomes of such classifiers to get class probabilities, from which one can compute the AUC.