Kao-Tai Tsai

Machine Learning for Knowledge Discovery with R

Methodologies for Modeling, Inference and Prediction

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Chapman and Hall/CRC / Taylor & Francis
• Seitenzahl: 262
• Erscheinungstermin: 25. September 2023
• Englisch
• Abmessung: 234mm x 156mm x 14mm
• Gewicht: 403g
• ISBN-13: 9781032071596
• ISBN-10: 1032071591
• Artikelnr.: 68715872

Autorenporträt

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.

Inhaltsangabe

1. Statistical Data Analysis. 2. Examining Data Distribution. 3. Regression with Shrinkage. 4. Recursive Partitioning Modeling. 5. Support Vector Machines. 6. Cluster Analysis. 7. Neural Networks. 8. Causal Inference and Matching. 9. Business and Commercial Data Modeling. 10. Analysis of Response Profiles.

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1. Statistical Data Analysis. 2. Examining Data Distribution. 3. Regression
with Shrinkage. 4. Recursive Partitioning Modeling. 5. Support Vector
Machines. 6. Cluster Analysis. 7. Neural Networks. 8. Causal Inference and
Matching. 9. Business and Commercial Data Modeling. 10. Analysis of
Response Profiles.

Rezensionen

"A knowledgeable applied statistician with good math skills will likely appreciate the brevity of this presentation, as well as its clear descriptions about how to easily apply the methods in R. This book is likely best used as a quick reference for those already familiar with these methods, for when one wants to aplly a particular machine learning method."

Amit K. Chowdhry, University of Rochester, USA, Royal Statistical Society, Series A: Statistics in Society.

"I will definitely recommend this book without any reservation to individuals in data science or associated disciplines that utilize machine learning and predictive modelling strategies for quantitatively making inference of data sets."

- Reuben Adatorwovor, ISCB News, September 2022.

"This book is a must-read for those involved in data science, machine learning, and statistical analysis. It provides the necessary tools and knowledge to understand and apply various techniques in data analysis. I highly recommend this book for academics, professionals, and enthusiasts interested in advancing their understanding of machine learning and statistical analysis. This book promises to enlighten readers on the theory and equip them with the practical skills to apply these concepts in real-world situations."

- Aszani Aszani, Universitas Gadjah Mada, Indonesia, Technometrics, November 2023.