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Optimize Fitted KNN Classifier

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Hyeongcheol Lee
Hyeongcheol Lee on 20 Nov 2019
Commented: Hyeongcheol Lee on 28 Nov 2019
I have questions about the "Optimize Fitted KNN Classifier" example (
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?


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Answers (1)

Bhargavi Maganuru
Bhargavi Maganuru on 28 Nov 2019
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'
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
Hope this helps!

  1 Comment

Hyeongcheol Lee
Hyeongcheol Lee on 28 Nov 2019
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?

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