Machine learning for knowledge discovery with R methodologies for modeling, inference and prediction / Kao-Tai Tsai, Frontier Informatics Services Bristol Myers Squibb Adjunct Professor, Jiann-Ping Hsu College of Public Health, Georgia Southern University.
| Author/creator | Tsai, Kao-Tai |
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Boca Raton : CRC Press, Taylor & Francis Group, 2022. |
| Description | xv, 244 pages : illustrations ; 25 cm |
| Supplemental Content | Full text available from Taylor & Francis eBooks |
| Subjects |
| Abstract | "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"-- Provided by publisher. |
| General note | "A Chapman & Hall book." |
| Bibliography note | Includes bibliographical references (pages 235-242) and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2021017126 |
| ISBN | 9781032065366 (hardback) |
| ISBN | 9781032071596 (paperback) |
| ISBN | (ebook) |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |