RapidMiner 9.7 is Now Available
Lots of amazing new improvements including true version control! Learn more about what's new here.
"Regarding KNN performance"
I am applying KNN with k=5. I split the data into two parts. One part is used in cross-validation and other is used to get the model from Cross-validation for testing.
I see that the Cross-validation performance is 0.619 (AUC) and for the test data set I separated its 0.812.
Is this because Cross-validation performance can be lower if some folds don't perform well?
Also, I learned that KNN is basically not a learning algorithm. which means it doesn't learn much from training but just uses the parameters to classify. Can this be the reason?