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"Regarding KNN performance"

varunm1varunm1 Moderator, Member Posts: 1,007   Unicorn
edited May 2019 in Help

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?


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