Scikit-fallback

Now **scikit-fallback** has a [documentation webpage](https://scikit-fallback.readthedocs.io/en/latest/)
and works w/ older versions of `scikit-learn`, `scipy`, and `numpy` (24, 26)!

See also the [v0.1.0](https://github.com/sanjaradylov/scikit-fallback/releases/tag/v0.1.0) release.

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:
python
>>> from skfb.experimental import enable_error_rejection_loss
>>> from skfb.metrics import error_rejection_loss

* Tuned multi-threshold fallback classifier w/ cross-validation:
python
>>> from skfb.experimental import enable_multi_threshold_fallback_classifier_cv
>>> from skfb.estimators import MultiThresholdFallbackClassifierCV
* Utility to summarize confidence scores class-wise.

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)

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.