Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction
Author:   Kao-Tai Tsai
Publisher:   Taylor & Francis Ltd
ISBN:  

9781032065366


Pages:   244
Publication Date:   15 September 2021
Format:   Hardback
Availability:   In Print   Availability explained
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Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction


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Author:   Kao-Tai Tsai
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.485kg
ISBN:  

9781032065366


ISBN 10:   1032065362
Pages:   244
Publication Date:   15 September 2021
Audience:   Professional and scholarly ,  General/trade ,  Professional & Vocational ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

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.

Reviews

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.


Author Information

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.

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