Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction - Brossura

Tsai, Kao-Tai

Brossura

ISBN 10: 1032071591 ISBN 13: 9781032071596

Casa editrice: Chapman and Hall/CRC, 2023

Riassunto

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein.

Key Features:

• Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies.
• Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations.
• Written by statistical data analysis practitioner for practitioners.

The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.

Su questo libro

Informazioni sull?autore

Kao-Tai Tsai obtained his Ph.D. in Mathematical Statistics from University of California, San Diego and had worked at AT&T Bell Laboratories to conduct statistical research, modelling, and exploratory data analysis. After that, he joined the US FDA and later pharmaceutical companies focusing on biostatistics, clinical trial research and data analysis to address the unmet needs in human health.

Dettagli dell'edizione

• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1032071591
• ISBN 13: 9781032071596
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 260
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile