Counterfactual ensembles for interpretable churn prediction: from real-world to privacy-preserving synthetic data - researchr publication

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Samuele Tonati, Marzio Di Vece, Fosca Giannotti, Roberto Pellungrini. Counterfactual ensembles for interpretable churn prediction: from real-world to privacy-preserving synthetic data. Machine Learning, 114(10):230, October 2025. [doi]

Abstract is missing.

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