Machine Learning for Knowledge Discovery with R

ISBN-13: 9781032071596 / Miękka / 2023 / 244 str.

Kao-Tai Tsai

Opis

‘Machine Learning for Knowledge Discovery with R’ contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes most recent supervised and unsupervised machine learning methodologies

Recenzje

"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.

Spis treści

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.

O autorze

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.