Created by: Lorenzo-Perini
All Submissions Basics:
-
Have you followed the guidelines in our Contributing document? Yes -
Have you checked to ensure there aren't other open Pull Requests for the same update/change? Yes -
Have you checked all Issues to tie the PR to a specific one? Yes
All Submissions Cores:
-
Have you added an explanation of what your changes do and why you'd like us to include them? Yes -
Have you written new tests for your core changes, as applicable? Yes -
Have you successfully ran tests with your changes locally? Yes -
Does your submission pass tests, including CircleCI, Travis CI, and AppVeyor? Yes -
Does your submission have appropriate code coverage? The cutoff threshold is 95% by Coversall. Yes
Original function in https://github.com/Lorenzo-Perini/Confidence_AD Published paper: https://people.cs.kuleuven.be/~lorenzo.perini/files/ExCeeD.pdf Reference: Lorenzo Perini, Vincent Vercruyssen, and Jesse Davis. Quantifying the confidence of anomaly detectors in their example-wise predictions. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 227-243. Springer, 2020.