Assessing the Suitability of Boosting Machine-Learning Algorithms for Classifying Arsenic-Contaminated Waters: A Novel Model-Explainable Approach Using SHapley Additive exPlanations
There is growing tension between high-performance machine-learning (ML) models and explainability within the scientific community. In arsenic modelling, understanding why ML models make certain predictions, for instance, “high arsenic” instead of “low arsenic”, is as important as the prediction accu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/21/3509 |