Scikit-activeml

We are happy to announce the following changes/features:

- We added `CogDQS` and `DBALStream` as two new stream-based query strategies (cf. 254).
- We implemented `BatchBALD` as a new pool-based query strategy (cf. 284).
- We added examples for the stream-based query strategies to the documentation and updated the notion of utilities, which now need to be maximized (cf. 286).
- We added legends to the examples for better understanding (cf. 287).
- We revised the developer guide (cf. 290).

We are happy to announce the following changes/features:

- We now support Python 3.10, and we deprecated Python 3.7 (cf. 279).
- We now support scikit-learn 1.2 (cf. 279).
- Update of probabilistic active learning to support mean bandwidth kernel (cf. 260).
- Added examples for DWUS and DUAL as two newly supported uncertainty-based query strategies (cf. 266).
- Updated QUIRE to allow sampling when mathematical assumptions are not fulfilled (cf. 274 ).

We are happy to announce the following changes/features:

- Improved test infrastructure
- Documentation improved
- Test coverage does not include test files anymore

We are happy to announce the following changes/features:

- Added pool-based query strategies for regression
- Fixed bug in vote entropy for query-by-committee
- Improved documentation

We are happy to announce the following changes/features:

- Added an alternative kernel frequency estimation option for StreamProbabilisticAL

We are happy to announce the following changes/features:

- Implementation of Discriminative Active Learning as a new pool-based query strategy
- Implementation of a new classifier wrapper to enable learning with sliding windows