Machine Learning with a Reject Option
BSD-3-CLAUSE License
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Published by sanjaradylov about 1 month ago
skfb.estimators.RateFallbackClassifierCV
accepts only one fallback rate (#11).skfb.metrics.PAConfusionMatrixDisplay
accepts rejector pipelines (#18).skfb.metrics.predict_reject_recall_score
(#14).skfb.estimators.multi_threshold_predict_or_fallback
and skfb.estimators.MultiThresholdFallbackClassifier
.skfb.estimators.AnomalyFallbackClassifier
(#13 and more)."ignore"
: don't return or store fallbacks (#16 and more).skfb.estimators.RateFallbackClassifierCV
accepts only one fallback rate (#11).>>> from skfb.experimental import enable_error_rejection_loss
>>> from skfb.metrics import error_rejection_loss
>>> from skfb.experimental import enable_multi_threshold_fallback_classifier_cv
>>> from skfb.estimators import MultiThresholdFallbackClassifierCV
skfb.estimators.ThresholdFallbackClassifierCV(fallback_mode="return")
(#4)skfb.estimators.ThresholdFallbackClassifierCV(fallback_mode="return")
(#4)skfb.estimators.ThresholdFallbackClassifier
(#6)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.