Common Mistakes Made in Cross Validation | Machine Learning

In this video we will discuss few of the common mistakes often made while performing cross validation of Machine Learning Models. While root means square error and accuracy rate are the two most popular metrics used in evaluating model performance in cross validation, there are limitations of using these when the performance is more important for the researcher in one section of the data than the other



For example we could be interested in better performance in predicting house price of a segment of the sample (say the middle priced houses) than the other segments. Similarly, we could be interested in predicting more default customers than the non default customers in a classification set up.