Full Description
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.
Contents
.- 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.
