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OverviewMachine 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. Full Product DetailsAuthor: Kao-Tai TsaiPublisher: Taylor & Francis Ltd Imprint: Chapman & Hall/CRC Weight: 1.070kg ISBN: 9781032065366ISBN 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  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 Contents1. 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.ReviewsA 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 InformationKao-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. Tab Content 6Author Website:Countries AvailableAll regions |
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