Omslagsbillede

Practical Statistical Learning and Data Science Methods Case Studies from LISA 2020 Global Network, USA /

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 immerse...

Fuld beskrivelse

Saved in:
Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Awe, O. Olawale (Editor), A. Vance, Eric (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Udgivelse:1st ed. 2025.
Serier:STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health,
Fag:
Artificial intelligence > Data processing.
Data mining.
Machine learning.
Biometry.
Data Science.
Data Mining and Knowledge Discovery.
Statistical Learning.
Biostatistics.
Online adgang:https://doi.org/10.1007/978-3-031-72215-8
Tags: Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!
Indholdsfortegnelse:
  • -- 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.

Lignende værker