For now, the unit tests for active learning algorithms are using the results of real-world data with fixed random seeds. So in the future if any modification to these algorithms have conflict with current test, it should be taken care carefully.
The rigorous way to do the test is to design artificial datasets. We'll leave it as future development goal.
For now, the unit tests for active learning algorithms are using the results of real-world data with fixed random seeds. So in the future if any modification to these algorithms have conflict with current test, it should be taken care carefully.
The rigorous way to do the test is to design artificial datasets. We'll leave it as future development goal.