Version v0.1.0 Release Notes (Initial Release)

🎉 Welcome to the first release of calibrated-explanations!

Features

Performance: Fast, reliable, stable and robust feature importance explanations.
Calibrated Explanations: Calibration of the underlying model to ensure that predictions reflect reality.
Uncertainty Quantification: Uncertainty quantification of the prediction from the underlying model and the feature importance weights.
Interpretation: Rules with straightforward interpretation in relation to the feature weights.
Factual and Counterfactual Explanations: Possibility to generate counterfactual rules with uncertainty quantification of the expected predictions achieved.
Conjunctive Rules: Conjunctive rules conveying joint contribution between features.

Multiclass Support: Multiclass support has been added since the original version developed for the paper Calibrated Explanations: with Uncertainty Information and Counterfactuals.
Regression Support: Support for explanations from standard regression was developed and is described in the paper Calibrated Explanations for Regression.
Probabilistic Regression Support: Support for probabilistic explanations from standard regression was added together with regression and is described in the paper mentioned above.
Conjunctive Rules: Since the original version, conjunctive rules has also been added.
Code Structure: The code structure has been improved a lot. The CalibratedExplainer, when applied to a model and a collection of test instances, creates a collection class, CalibratedExplanations, holding CalibratedExplanation objects, which are either FactualExplanation or CounterfactualExplanation objects. Operations can be applied to all explanations in the collection directly through CalibratedExplanations or through each individual CalibratedExplanation (see the documentation).

To install this version, you can use pip:

pip install calibrated-explanations==0.1.0