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Comparative analysis of explainable machine learning prediction models for hospital mortality
77
Zitationen
4
Autoren
2022
Jahr
Abstract
We already know that ML models treat data differently depending on the underlying algorithm. Our comparative analysis visualises implications of these differences and their importance in a healthcare setting. SHAP value analysis is a promising method for incorporating explainability in model development and usage and might yield better and more trustworthy ML models in the future.
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