Functions to estimate the expected best-out-of-n result from a set of validation and test results.
Functions to estimate the expected best-out-of-n (Boon) result from a set of validation and test results for a machine learning architecture. The measure is fully described in the paperBajgar, O., Kadlec, R., and Kleindienst, J. A Boo(n) for Evaluating Architecture Performance. ICML 2018.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|boon-0.1.0-py3-none-any.whl (2.9 kB) Copy SHA256 hash SHA256||Wheel||py3|
|boon-0.1.0.tar.gz (2.9 kB) Copy SHA256 hash SHA256||Source||None|