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Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
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.
"About this title" may belong to another edition of this title.
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Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction
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Chapman and Hall/CRC 2021-09-24, 2021
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