Comments (1)
For unbalanced data, where the estimator hasn't been trained on minority classes, typically the uncertainty measure fails to give epistemic uncertainty so won't (necessarily) sample the minority classes. Unlike uncertainty-based active learning, diversity-based AL handles this well. I've produced some diversity-based implementations privately and will look to submit a PR in the near future.
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Related Issues (20)
- Can I use modAL with estimators from other libraries than scikit-learn like xgboost? HOT 1
- Encountering error with number of batches per epoch
- mmdetection integration with modAL
- Adding active learning regression implementations based on greedy sampling HOT 2
- modAL not installable via pypi anymore HOT 3
- the modAL package has been changed into modal in the pip repository HOT 7
- Data augmentation with `skorch`
- QBC approach for multi-class classification
- Suggestion on how to improve acquisition.UCB for active GP example HOT 1
- QBC stratified bootstrapping HOT 1
- Use modAL on BERT models HOT 1
- Spacy NER HOT 1
- raise ImportError( ImportError: C extension: None not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext' to build the C extensions first.
- uncertainty query for 2d classifier output
- The installation guide in the docs is wrong HOT 4
- Python
- Issue with Macro Average F1 Score in modAL for Multi-Class Classification
- Some of the step tasks have been OOM Killed.
- Support for pyspark.ml.classification estimators
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