Practical Statistical Learning and Data Science Methods
Case Studies from LISA 2020 Global Network, USA
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Beschreibung
This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science.
Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike.
The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as “stat labs.” These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.
This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science.
Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike.
The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as “stat labs.” These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.
Presents cutting-edge statistical methods and techniques employed in real-world applications Embodies common experiences gathered within the LISA 2020 Global Network Provides actionable insights for practitioners and researchers
Autor*in
O. Olawale Awe
Themen in »Practical Statistical Learning and Data Science Methods«
statistical learning data science machine learning data classification time series machine learning classifiers predictive modeling deep learning models SHAP LIME Shapley Additive Explanation Local Interpretable Model-agnostic Explanations