Machine Learning Zoomcamp Update:Thursday, 30 September 2021
Date: 30 September 2021
Today, I completed the Sessions 4.5, 4.6 and 4.7.
Session 4.5  ROC Curves
This session explained about ROC curve.
Key takeaways:
 ROC curve stands for Receiver Operating Characteristic curve.

False Positive Rate (FPR) = FP/(TN+FP)
True Positive Rate (TPR) = TP/(FN+TP)  TPR = Recall
Session 4.6  ROC AUC
This session explained ROC AUC (Area Under Curve).
Key takeaways:
 ROC AUC >= 0.5, if it is not true then invert the predictions.

Ideal model ROC AUC = 1
Random model ROC AUC = 0.5 
Probability that a randomly selected positive (true) example has higher prediction probability value than a randomly selected negative example = AUC
P(randomly selected positive example > randomly selected negative example) = AUC
Session 4.7  CrossValidation
This session gave an introduction to Kfold cross validation.
Estimated Time Taken: 1 hour 30 minutes