👆🏻What is AUC - ROC Curve and why it is so important to evaluate quality of ML-model?
Area Under the Receiver Operating Characteristics is evaluation metric is used to check or visualize the performance of the multi-class classification problem.
AUC - ROC curve measures a performance of the classification at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1. Higher the AUC, the better the model is at distinguishing between patients with the disease and no disease.
An excellent model has AUC near to the 1 which means it has a good measure of separability. A poor model has an AUC near 0 which means it has the worst measure of separability. In fact, it means it is reciprocating the result. It is predicting 0s as 1s and 1s as 0s. And when AUC is 0.5, it means the model has no class separation capacity whatsoever.
Sensitivity and Specificity are inversely proportional to each other. So when we increase Sensitivity, Specificity decreases, and vice versa. When we decrease the threshold, we get more positive values thus it increases the sensitivity and decreasing the specificity. Similarly, when we increase the threshold, we get more negative values thus we get higher specificity and lower sensitivity.
In a multi-class model, we can plot the N number of AUC ROC Curves for N number classes using the One vs ALL methodology. So for example, If you have three classes named X, Y, and Z, you will have one ROC for X classified against Y and Z, another ROC for Y classified against X and Z, and the third one of Z classified against Y and X.
https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5