scikit-fallback

Machine Learning with a Reject Option

BSD-3-CLAUSE License

Downloads
346
Stars
1
Committers
1

Bot releases are visible (Hide)

scikit-fallback - v0.1.0

Published by sanjaradylov about 1 month ago

Fixes

  • 🐛 skfb.estimators.RateFallbackClassifierCV accepts only one fallback rate (#11).
  • 🐞 skfb.metrics.PAConfusionMatrixDisplay accepts rejector pipelines (#18).

Features

Stable

  • Predict-reject recall score skfb.metrics.predict_reject_recall_score (#14).
  • New estimators accept fitted estimators and don't require refitting for inference.
  • Support for scikit-learn>=1.0,<=1.2.
  • Multi-threshold fallback classification: skfb.estimators.multi_threshold_predict_or_fallback and skfb.estimators.MultiThresholdFallbackClassifier.
  • Fallback classification based on anomaly detection: skfb.estimators.AnomalyFallbackClassifier (#13 and more).
  • Fallback mode "ignore": don't return or store fallbacks (#16 and more).

Experimental

  • skfb.estimators.RateFallbackClassifierCV accepts only one fallback rate (#11).
  • Error-fallback loss:
    >>> from skfb.experimental import enable_error_rejection_loss
    >>> from skfb.metrics import error_rejection_loss
    
  • Tuned multi-threshold fallback classifier w/ cross-validation:
    >>> from skfb.experimental import enable_multi_threshold_fallback_classifier_cv
    >>> from skfb.estimators import MultiThresholdFallbackClassifierCV
    
  • Utility to summarize confidence scores class-wise.
scikit-fallback - v0.0.1 Latest Release

Published by sanjaradylov 3 months ago

Bug fixes

  • Incorrect masking of fallbacks of ambiguity-threshold-based rules (#3)
  • Errors when fitting skfb.estimators.ThresholdFallbackClassifierCV(fallback_mode="return") (#4)

Improvements

  • Passing scikit-learn metrics as scorers in skfb.estimators.ThresholdFallbackClassifierCV(fallback_mode="return") (#4)
  • Inference w/o training the fitted base estimator of skfb.estimators.ThresholdFallbackClassifier (#6)
scikit-fallback - 🎉 Let's get ready to rumble!

Published by sanjaradylov 4 months ago

This is the first release of scikit-fallback and it implements rudimentary tools for supporting and evaluating rejections in classification problems:

  • sfkb.estimators.ThresholdFallbackClassifier(CV) and RateFallbackClassifier for (meta-)classification w/ a reject option.
  • skfb.metrics for Predict-Fallback metrics, confusion matrices, and curves.
  • skfb.core.array for NDArray-compatible FBNDArray for storing predictions and fallback masks.
Package Rankings
Top 35.71% on Pypi.org
Badges
Extracted from project README
PythonVersion Black linting: pylint
Related Projects