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Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data
Published 2025-04-01“…Performance was assessed using metrics such as coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula>), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.55, MAE = 0.17, and RMSE = 0.05). …”
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Model Predictive Control of Uncertain Constrained Linear System Based on Mixed ℋ2/ℋ∞ Control Approach
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The value of pregnancy-related factors in the prediction of cardiovascular disease: a systematic review
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Predictive Processing in Autism Spectrum Disorder: The Atypical Iterative Prior Updating Account
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XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine
Published 2024-12-01Get full text
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