What is ROC AUC and why is it important in machine learning?

What is ROC AUC and why is it important in machine learning?

ROC AUC stands for Receiver Operating Characteristic Area Under the Curve. It's a performance measurement for classification problems.

How do you interpret the ROC AUC value?

The ROC AUC value ranges from 0 to 1. A value of 0.5 indicates no discrimination, while 1 indicates perfect discrimination.

What are the advantages of using ROC AUC over accuracy?

ROC AUC is more informative than accuracy, especially with imbalanced datasets, as it considers true positive and false positive rates.

Can you give an example of how to calculate ROC AUC?

Sure! You can calculate ROC AUC using libraries like scikit-learn in Python, which provides functions to compute it easily.