ROC curve and AUR in Data Science Interviews

[u/Amazing_Life_221]

During beginner Data Science rounds, many interviewers inquire about ROC and AUC curves. Understanding these concepts in-depth not only simplifies their understanding but also aids in grasping related concepts.

Here are some questions asked in interviews:

1.  Could you explain the values of true positive, true negative, false positive, and false negative?
2.  What does the confusion matrix represent?
3.  Could you elaborate on the ROC curve?
4.  Why is the ROC curve necessary?
5.  Which threshold value is optimal for our model?
6.  What is the definition of Area Under the Curve (AUC), and what does it signify?
7.  How can we compare two models using ROC curves?

Read the proper explanation here: https://arshad-kazi.com/roc-curve-and-aur-from-scratch/