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CrystalCandle: A User-Facing Model Explainer for Narrative Explanations
[article]
2022
arXiv
pre-print
Predictive machine learning models often lack interpretability, resulting in low trust from model end users despite having high predictive performance. While many model interpretation approaches return top important features to help interpret model predictions, these top features may not be well-organized or intuitive to end users, which limits model adoption rates. In this paper, we propose CrystalCandle, a user-facing model explainer that creates user-digestible interpretations and insights
arXiv:2105.12941v3
fatcat:ftvd5ivjt5cy7mvu6i52gi7oaa