Optimize Fitted KNN Classifier

I have questions about the "Optimize Fitted KNN Classifier" example (https://www.mathworks.com/help/stats/fitcknn.html).

Is there any rule to change values of "NumNeighbors" and "Distance" while iteration?

It just seems random.

And, I'm not sure the final model with below "NumNeighbors" and "Distance" is the best optimized model or not.

How can you assure the final model is the best optimized?

Answer 1

[Bhargavi Maganuru]

Values of “NumNeighbors” and “Distance” are changing because of setting property ‘OptimizeHyperparameters' to ‘auto’ which will try to optimize the “distance” and “NumNeighbours” parameters.

Refer to the following link for more information about ‘OptimizeHyperparameters'

https://www.mathworks.com/help/stats/fitcknn.html#d117e332170

To pick best estimate, bayesian optimization acquisition function 'expected-improvement-plus' is used. It calculates best estimated feasible point using ‘bestpoint’ function.

For more information about ‘bestpoint’ refer the link

https://www.mathworks.com/help/stats/bayesianoptimization.bestpoint.html

Hope this helps!

[Hyeongcheol Lee]

Hello, Bhargavi.

Thanks for your reply.

But, I'm still not sure about below two things.

1. Is there any rule to change values of "NumNeighbors" and "Distance" while iteration? In other words, is there any rule to choose "Next point"?

2. In the example "Optimize Fitted KNN Classifier" in the below site, the iteration is only 30. So, if we do more iteration I think there is better optimization. So, my question is... how should I do to get real optimization point?

https://www.mathworks.com/help/stats/fitcknn.html#d117e332170