This release includes multiple features, improvements and bug fixes. Please refer to the
for a detailed list of changes.
Summary of changes
Delayed labels for supervised learning
- Add support for delayed labels in streams and evaluations. Two new methods are available for this purpose:
- Adaptive Random Forest Regressor
Note: This implementation is slightly different from the original algorithm. The Hoeffding Tree Regressor is used as
the base learner, instead of the FIMT-DD. It also adds a new strategy to monitor the incoming data and check for concept
drifts. For more information, see the notes in the documentation.
implementation for regression.
a drift detection method based on the Hoeffding’s bounds with moving average-test.
a drift detection method based on the Hoeffding’s bounds with moving weighted-average-test.
- KSWIN (Kolmogorov-Smirnov Windowing) concept drift detector.
Efficiency and enhancements
- Improve efficiency for metrics calculations (classification).
- Add support for multi-class metrics: precision, recall, F1-score, and G-mean.
- Reduce substantially the size of the package.
- Enhance handling of categorical attributes in Hoeffding Trees.
- Add bootstrap option in the Hoeffding Adaptive Tree classifier.
Bug fixes and API changes
This release includes a set of bug fixes and API changes. The most relevant API change is the renaming of multiple
methods following a more consistent and informative naming convention. The full list of bug fixes and details of
API changes is available in the changelog entry.
- Add support for Python 3.8.
- This is the last version that supports Python 3.5 as it is reaching its end-of-life date (2020-09-13).
Patch release note
New features and improvements were introduced in version
0.5.3 only includes the fix for a bug that
triggered an error in the
conda-forge distribution files for Windows.