Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Statistical evaluation of the results #481

Open
mlaky88 opened this issue Mar 4, 2024 · 3 comments
Open

Statistical evaluation of the results #481

mlaky88 opened this issue Mar 4, 2024 · 3 comments

Comments

@mlaky88
Copy link
Contributor

mlaky88 commented Mar 4, 2024

It would be great if the results could be statsticially evaluated after all experiments are done. At least the Friedman and Wilcox tests?

I think it should be made possible that results of other algorithms could be supplied in order to do the statistical test? For example if I would have my own algorithm for optimization, and would not be bothered to include it into the Niapy library, I would like to have an option to provide my own results in a specific format, which could be used in statistical evaluation against the algorithms in Niapy. I hope I made sense.

@rhododendrom
Copy link
Contributor

+1

1 similar comment
@firefly-cpp
Copy link
Contributor

+1

@peacemo
Copy link
Contributor

peacemo commented Nov 1, 2024

Scipy is good enough. 😁

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

4 participants