Maybe wrong values for precision and recall I'm trying to do some data mining with RapidMiner studio. 
I've applied the K-nearest neighbor algorithm with different values of K. 
As I expected, accuracy increase and after K=5, it decrease. But I cannot understand why value of recall for Basic increase (as I expected) but recall for Premium decrease. The same for values of precision. 

Below my results:
Basic and Premium are the values of my class label

With K=5

True Basic
Class recall: 91.83%
Class precision: 81.18%

True Premium
Class recall: 32.87%
Class precision: 56.07%



With K=2

True Basic
Class recall: 81.99%
Class precision: 82.94%

True Premium
Class recall: 32.87%
Class precision: 45.20%
If you are calling a higher proportion of cases as Basic with K=5, then this will probably lower your Basic precision, increase your Basic recall, increase your Premium precision, and lower your Premium recall. This is because there is always a tradeoff between precision and recall, and Basic and Premium are opposites in your binary classifier.