Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification
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DOI: 10.1101/2022.09.19.508550
Abstract: Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and… read more here.
Keywords: tree based; shapley additive; additive explanations; machine learning ... See more keywords
Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive exPlanation (SHAP)
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DOI: 10.3390/fi15030088
Abstract: The need for artificial intelligence (AI) and machine learning (ML) models to optimize data center (DC) operations increases as the volume of operations management data upsurges tremendously. These strategies can assist operators in better understanding… read more here.
Keywords: shap; shapley additive; machine learning; additive explanation ... See more keywords
Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods
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DOI: 10.3390/ma15207344
Abstract: This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for… read more here.
Keywords: strength; impact raw; learning shapley; shapley additive ... See more keywords