Open perellonieto opened 7 years ago
As a general note, possibly you could support the base classes in sklearn (ClassifierMixin, RegressorMixin) rather than individual methods, and then the actual methods could be passed as a parameter (possibly a string, although that's ugly) which would mean less work on your end.
Commit 3260fe6518734fc3be6921ea0324510c179d59dd adds a tool for anomaly detection. That is, it uses a model that can be trained in an unsupervised way and generates scores at every stream. Because the model is passed as a parameter, it is not only anomaly detection but could be density estimation, outlier detection, clustering, or any other unsupervised method. The tool should be refactored in order to indicate that.
Open #3 to refactor the anomaly detection tool into unsupervised learning.
Some easy to add tools could be density estimation models that learn the distribution of the data without any labels.
Also, one class classification models, novelty or anomaly detection algorithms.