John Tuhao Chen, Clement Lee, Lincy Y. Chen

Statistical Prediction and Machine Learning (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Erscheinungstermin: 6. August 2024
• Englisch
• ISBN-13: 9781040096338
• Artikelnr.: 72271287

Autorenporträt

John T. Chen is a professor of Statistics at Bowling Green State University. He completed his postdoctoral training at McMaster University (Canada) after earning a PhD degree in statistics at the University of Sydney (Australia). John has published research papers in statistics journals such as Biometrika as well as in medicine journals such as the Annals of Neurology.

Clement Lee is a data scientist in a private firm in New York. He earned a Master's degree in applied mathematics from New York University, after graduating from Princeton University in computer science. Clement enjoys spending time with his beloved wife Belinda and their son Pascal.

Lincy Y. Chen is a data scientist at JP Morgan Chase & Co. She graduated from Cornell University, winning the Edward M. Snyder Prize in Statistics. Lincy has published papers regarding refinements of machine learning methods.

Inhaltsangabe

Preface 1. Two Cultures in Data Science 2. Fundamental Instruments 3. Sensitivity and Specificity Trade-off 4. Bias and Variation Trade-off 5. Linear Prediction 6. Nonlinear Prediction 7. Minimum Risk Classification 8. Support Vectors and Duality Theorem 9. Decision Trees and Range Regressions 10. Unsupervised Learning and Optimization 11. Simultaneous Learning and Multiplicity Bibliography Index

Rezensionen

"This book is a valuable resource for a diverse audience, including students, researchers, and professionals in the fields of statistics, data science, and machine learning. Its ability to synthesize theoretical principles with practical applications sets it apart from many other texts in the domain. The authors strike a fine balance between addressing foundational concepts, such as bias-variance tradeoffs and cross-validation, and delving into advanced topics like support vector machines and simultaneous learning. By integrating these elements, the book equips readers with a robust toolkit for tackling real-world challenges. Particularly commendable is its emphasis on practical examples, such as medical case studies, which not only contextualize the theoretical discussions but also demonstrate their relevance across disciplines. For professionals, the book offers insights into optimizing models and applying techniques to complex datasets, bridging the often-intimidating gap between theory and practice." - Fransiskus Serfian Jogo, Techonometrics, February 2025