國立虎尾科技大學 |

Practical statistical learning and data science methods = case studies from LISA 2020 Global Network, USA /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Practical statistical learning and data science methods/ edited by O. Olawale Awe, Eric A. Vance.
其他題名:	case studies from LISA 2020 Global Network, USA /
其他作者:	Vance, Eric Alan.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xxix, 752 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Biostatistics. -
電子資源:	https://doi.org/10.1007/978-3-031-72215-8
ISBN:	9783031722158

Practical statistical learning and data science methods = case studies from LISA 2020 Global Network, USA /
Practical statistical learning and data science methodscase studies from LISA 2020 Global Network, USA /[electronic resource] :edited by O. Olawale Awe, Eric A. Vance. - Cham :Springer Nature Switzerland :2025. - xxix, 752 p. :ill. (chiefly color), digital ;24 cm. - STEAM-H: science, technology, engineering, agriculture, mathematics & health,2520-1948. - STEAM-H: science, technology, engineering, agriculture, mathematics & health..

Effects of Imputation Techniques on Predictive Performance of Supervised Machine Learning Algorithms: Empirical Insights from Health Data Classification. -- Predicting Air Quality in an Urban African City Using Four Comparative Novel Time Series Models. -- Obesity Classification Using Weighted Hard and Soft Voting Ensemble Machine Learning Classifiers. -- Predictive Modeling for Disease Diagnosis Using Calibrated Algorithms: A Comparative Study. -- Predicting Precipitation Dynamics in Africa Using Deep Learning Models. -- Enhancing Predictive Performance through Optimized Ensemble Stacking for Imbalanced Classification Problems. -- A Comparative Exploration of SHAP and LIME for Enhancing the Interpretability of Machine Learning Models in BMI Classification. -- Decision Tree Planning Strategies for Predicting Obesity. -- Clustering Multiple Time Series with SSA. -- Spine-Based Calibration for Classification Algorithms: An Experimental Comparison of Various Imbalanced Ratios. -- Exploring the Applicability of Advanced Exponential Smoothing and NN Models for Climate Time Series Forecasting: Insights and Changepoint Prediction in the Brazilian Context. -- A Comprehensive Forecasting Experiment on Temperature Trends Across Thirty-Two American Countries. -- A Comparative Analysis of Sampling Methods for Imbalanced Data Classification in Machine Learning Health Applications. -- Comparative Analysis of MCC, F1-Score, and Balanced Accuracy Metrics for Imbalanced Health Data Classification. -- Basics of R- Shiny for developing Interactive Visualizations.

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.

ISBN: 9783031722158

Standard No.: 10.1007/978-3-031-72215-8doiSubjects--Topical Terms:

783654
Biostatistics.

LC Class. No.: HA12

Dewey Class. No.: 300.151

Practical statistical learning and data science methods = case studies from LISA 2020 Global Network, USA /
LDR:04117nam a2200337 a 4500 001 1160503
003 DE-He213
005 20241228115249.0
006 m o d
007 cr nn 008maaau
008 251029s2025 sz s 0 eng d
020 $a 9783031722158 $q (electronic bk.)
020 $a 9783031722141 $q (paper)
024 7 $a 10.1007/978-3-031-72215-8 $2 doi
035 $a 978-3-031-72215-8
040 $a GP $c GP
041 0 $a eng
050 4 $a HA12
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 300.151 $2 23
090 $a HA12 $b .P895 2025
245 0 0 $a Practical statistical learning and data science methods $h [electronic resource] : $b case studies from LISA 2020 Global Network, USA / $c edited by O. Olawale Awe, Eric A. Vance.
260 $a Cham : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xxix, 752 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a STEAM-H: science, technology, engineering, agriculture, mathematics & health, $x 2520-1948
505 0 $a Effects of Imputation Techniques on Predictive Performance of Supervised Machine Learning Algorithms: Empirical Insights from Health Data Classification. -- Predicting Air Quality in an Urban African City Using Four Comparative Novel Time Series Models. -- Obesity Classification Using Weighted Hard and Soft Voting Ensemble Machine Learning Classifiers. -- Predictive Modeling for Disease Diagnosis Using Calibrated Algorithms: A Comparative Study. -- Predicting Precipitation Dynamics in Africa Using Deep Learning Models. -- Enhancing Predictive Performance through Optimized Ensemble Stacking for Imbalanced Classification Problems. -- A Comparative Exploration of SHAP and LIME for Enhancing the Interpretability of Machine Learning Models in BMI Classification. -- Decision Tree Planning Strategies for Predicting Obesity. -- Clustering Multiple Time Series with SSA. -- Spine-Based Calibration for Classification Algorithms: An Experimental Comparison of Various Imbalanced Ratios. -- Exploring the Applicability of Advanced Exponential Smoothing and NN Models for Climate Time Series Forecasting: Insights and Changepoint Prediction in the Brazilian Context. -- A Comprehensive Forecasting Experiment on Temperature Trends Across Thirty-Two American Countries. -- A Comparative Analysis of Sampling Methods for Imbalanced Data Classification in Machine Learning Health Applications. -- Comparative Analysis of MCC, F1-Score, and Balanced Accuracy Metrics for Imbalanced Health Data Classification. -- Basics of R- Shiny for developing Interactive Visualizations.
520 $a 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.
650 2 4 $a Biostatistics. $3 783654
650 2 4 $a Statistical Learning. $3 1396099
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 1 4 $a Data Science. $3 1174436
650 0 $a Machine learning. $3 561253
650 0 $a Statistics $x Methodology. $3 1008148
650 0 $a Statistics. $3 556824
700 1 $a Vance, Eric Alan. $3 1436245
700 1 $a Awe, O. Olawale. $3 1436244
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a STEAM-H: science, technology, engineering, agriculture, mathematics & health. $3 1206408
856 4 0 $u https://doi.org/10.1007/978-3-031-72215-8
950 $a Mathematics and Statistics (SpringerNature-11649)