calibrated-explanations 0.11.0

Extract calibrated explanations from machine learning models.

calibrated-explanations

Trustworthy AI explanations with uncertainty intervals and counterfactuals, for any scikit-learn model.

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What Problem Does It Solve?

Most XAI tools — LIME, SHAP — explain whatever the model outputs. If the model's predicted probabilities are miscalibrated (and they often are, especially for tree-based models trained without a calibration step), the explanations inherit that miscalibration. The feature weights reflect an overconfident model, not a reliable signal you can act on.

calibrated-explanations fixes this at the root. It calibrates the model first — using Venn-Abers predictors for classification and Conformal Predictive Systems for regression — then explains the calibrated output. Every explanation includes a calibrated uncertainty interval that shows how confident the model actually is, not just a point estimate that hides model uncertainty.

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What Does the Output Look Like?

Calling explanation[0].to_narrative(output_format="text", expertise_level="advanced") returns a structured text narrative. The output below is (illustrative) — a loan-approval context using the exact format produced at runtime:

Factual Explanation:
--------------------------------------------------------------------------------
Prediction: APPROVE
Calibrated Probability: 0.840
Prediction Interval: [0.710, 0.930]

Factors impacting the calibrated probability for class APPROVE positively:
annual_income (45200) >= 45000 — weight ≈ +0.312 [+0.198, +0.421]
credit_history_years (5.2) >= 5 — weight ≈ +0.187 [+0.091, +0.284]
outstanding_debt (2800) < 3000 — weight ≈ +0.143 [+0.055, +0.231]
employment_status (permanent) = permanent — weight ≈ +0.098 [+0.012, +0.185]

Factors impacting the calibrated probability for class APPROVE negatively:
missed_payments (3) > 2 — weight ≈ -0.201 [-0.334, -0.068]

• The Prediction Interval [0.710, 0.930] shows the calibrated uncertainty range — narrow means high confidence, wide (e.g., [0.12, 0.89]) means the model is uncertain and the decision should be treated with caution.
• Each factor line shows the observed value, the matching rule condition, the signed weight (positive = pushes toward the predicted class), and the weight's own uncertainty interval — all computed from calibrated probabilities, not raw model scores.

Calling alt[0].to_narrative(output_format="text", expertise_level="advanced") on the result of explore_alternatives shows what needs to change to flip or reinforce the decision, with each alternative backed by a calibrated interval:

Alternative Explanations:
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Prediction: APPROVE
Calibrated probability: 0.840
Prediction Interval: [0.710, 0.930]

Alternatives to increase the calibrated probability for class APPROVE:
- If missed_payments <= 1 then 0.921 [0.856, 0.970]
- If outstanding_debt < 2000 then 0.893 [0.814, 0.949]

Alternatives to decrease the calibrated probability for class APPROVE:
- If annual_income < 30000 then 0.518 [0.344, 0.686]
- If credit_history_years < 2 then 0.601 [0.447, 0.752]

• Each alternative line shows the rule that would need to hold, the resulting calibrated probability if that rule were satisfied, and the uncertainty interval on that alternative — narrow means the model is confident even in the counterfactual region.

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Quick Start

pip install calibrated-explanations

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from calibrated_explanations import WrapCalibratedExplainer

d = load_breast_cancer()
X_tr, X_te, y_tr, y_te = train_test_split(d.data, d.target, test_size=0.2, stratify=d.target, random_state=42)
X_pr, X_cal, y_pr, y_cal = train_test_split(X_tr, y_tr, test_size=0.25, stratify=y_tr, random_state=42)
explainer = WrapCalibratedExplainer(RandomForestClassifier(random_state=42))
explainer.fit(X_pr, y_pr);
explainer.calibrate(X_cal, y_cal, feature_names=d.feature_names)
exp = explainer.explain_factual(X_te[:1])
print(exp[0].to_narrative(output_format="text", expertise_level="advanced"))
exp[0].plot()

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What Can It Explain?

Task	Description	Key Method
Binary classification	Binary yes/no decision with calibrated probabilities	explain_factual, explore_alternatives
Multiclass	Multiclass classification (3+ classes), per-class explanations	explain_factual, explore_alternatives
Regression with intervals	Predict a value with a conformal uncertainty interval defined by given percentiles	explain_factual(low_high_percentiles=(5, 95)), explore_alternatives(low_high_percentiles=(10, 90))
Probabilistic regression	Explain a probability query on a regression target (e.g., $P(y \le t)$ or $P(t_l < y \le t_h)$ )	explain_factual(threshold=t), explore_alternatives(threshold=(t_l, t_h))

All four modes use the same API — the wrapper infers classification vs regression from the underlying estimator (or you pass mode to calibrate), and you add threshold only for probabilistic regression.

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What Makes It Different?

Feature	LIME	SHAP	calibrated-explanations
Calibrated probabilities and predictions	No	No	Yes
Uncertainty intervals per explanation	No	No	Yes
Built-in counterfactual / alternative rules	No	No	Yes
Deterministic (stable) output	No	High (TreeExplainer)	Yes
Uncertainty-qualified counterfactuals (Ensured framework)	No	No	Yes
Conditional calibration by group	No	No	Yes

Honest limitation: CE does not currently provide global feature importance rankings — tasks requiring aggregated SHAP-style importance plots should use SHAP for that component.

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Uncertainty-Qualified Counterfactuals (Ensured)

Standard counterfactual methods tell you "change feature X to value Y and the decision flips." They do not tell you whether the model is actually confident about that alternative scenario. The counterfactual may point to a region of input space where the model has seen very little training data, meaning the flip is a formal artefact, not a reliable prediction.

CE's Ensured framework (Löfström et al., arXiv:2410.05479) addresses this directly. The ensured_explanations() filter keeps only counterfactuals where the model's calibrated interval lies fully in the opposite class — providing formal coverage evidence that the alternative decision is not merely a point estimate crossing the class boundary. The result: every surfaced counterfactual is backed by conformal guarantees.

explainer.explore_alternatives(X_query)[0].ensured_explanations()  # X_query: array-like, shape (n_samples, n_features)

Read more: Ensured explanations playbook · Alternatives concept guide

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Fairness-Aware Explanations

A model can be globally well-calibrated but systematically overconfident for a minority group. CE's Mondrian/conditional calibration conditions calibration and uncertainty on a per-instance group label (bins) (Löfström & Löfström, xAI 2024). The result: explanation uncertainty intervals are valid within each group, not only on average. Wider intervals for a group are a direct, auditable signal of data insufficiency — a concrete fairness artefact that can be reported to regulators or risk committees.

explainer.explain_factual(X_query, bins=X_query[:, gender_col_index])

Read more: Mondrian / conditional calibration playbook

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Research and Citations

calibrated-explanations is the product of peer-reviewed research. If you use it, please cite the relevant paper(s).

1. Löfström, H., Löfström, T., Johansson, U., Sönströd, C. (2024). "Calibrated Explanations: with Uncertainty Information and Counterfactuals." Expert Systems with Applications. doi:10.1016/j.eswa.2024.123154

2. Löfström, T., Löfström, H., Johansson, U., Sönströd, C., Matela, R. (2025). "Calibrated Explanations for Regression." Machine Learning 114, 100. Springer Nature. doi:10.1007/s10994-024-06642-8

3. Löfström, H., Löfström, T. (2024). "Conditional Calibrated Explanations: Finding a Path Between Bias and Uncertainty." xAI 2024, Communications in Computer and Information Science, vol 2153. Springer, Cham. doi:10.1007/978-3-031-63787-2_17

4. Löfström, T., Löfström, H., Johansson, U. (2024). "Calibrated Explanations for Multi-class." Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications, Proceedings of Machine Learning Research, PMLR 230:175–194.

5. Löfström, H., Löfström, T., Hallberg Szabadvary, J. (2024). "Ensured: Explanations for Decreasing the Epistemic Uncertainty in Predictions." arXiv:2410.05479. https://arxiv.org/abs/2410.05479

6. Löfström, H. et al. (2023). "Investigating the impact of calibration on the quality of explanations." Annals of Mathematics and Artificial Intelligence.

BibTeX entries are available in CITATION.cff.

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Install & Requirements

pip install calibrated-explanations

• Python ≥ 3.8
• scikit-learn ≥ 1.3
• crepes ≥ 0.8.0 (conformal calibration backend)
• venn-abers ≥ 1.4.0 (Venn-Abers calibration)
• numpy ≥ 1.24, pandas ≥ 2.0 (standard data science stack)

Optional: matplotlib is required only for .plot() visualisation calls.

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Documentation

• Full documentation
• Getting started in 60 seconds
• Contributing

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License

Released under the BSD 3-Clause License — open for both academic and commercial use without restriction.

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Acknowledgements

Development of calibrated-explanations has been funded by the Swedish Knowledge Foundation together with industrial partners supporting the research and education environment on Knowledge Intensive Product Realization SPARK at Jönköping University, Sweden, through projects: AFAIR grant no. 20200223, ETIAI grant no. 20230040, and PREMACOP grant no. 20220187. Helena Löfström was initially a PhD student in the Industrial Graduate School in Digital Retailing (INSiDR) at the University of Borås, funded by the Swedish Knowledge Foundation, grant no. 20160035.