Machine learning for knowledge discovery with R [electronic resource] : 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.
"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 regress...
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
Boca Raton :
CRC Press,
2022.
|
| Edition: | First edition. |
| Subjects: | |
| Online Access: | |
| Variant Title: |
Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction |
| Format: | Electronic eBook |
MARC
| LEADER | 00000nam a22000003i 4500 | ||
|---|---|---|---|
| 001 | ebs29397513e | ||
| 003 | EBZ | ||
| 006 | m o d |||||| | ||
| 007 | cr|unu|||||||| | ||
| 008 | 210607s2022 flu ob 001 0 eng | ||
| 020 | |z 9781032065366 | ||
| 020 | |z 9781032071596 | ||
| 020 | |a 9781000450279 (online) | ||
| 020 | |a 9781000450354 (online) | ||
| 020 | |a 9781003205685 (online) | ||
| 035 | |a (EBZ)ebs29397513e | ||
| 040 | |a DLC |b eng |d EBZ | ||
| 042 | |a pcc | ||
| 050 | 0 | 0 | |a QA76.9.D343 |
| 100 | 1 | |a Tsai, Kao-Tai, |e author. | |
| 245 | 1 | 0 | |a Machine learning for knowledge discovery with R |h [electronic resource] : |b methodologies for modeling, inference and prediction / |c Kao-Tai Tsai, Frontier Informatics Services Bristol Myers Squibb Adjunct Professor, Jiann-Ping Hsu College of Public Health, Georgia Southern University. |
| 246 | 2 | |a Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction | |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton : |b CRC Press, |c 2022. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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"-- |c Provided by publisher. | ||
| 650 | 0 | |a Data mining |x Methodology. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 773 | 0 | |t STATSnetBASE |d Taylor and Francis | |
| 776 | 0 | 8 | |i Print version: |a Tsai, Kao-Tai. |t Machine learning for knowledge discovery with R |b First edition. |d Boca Raton : CRC Press, 2021 |z 9781032065366 |w (DLC) 2021017126 |
| 856 | 4 | 0 | |y Access Content Online(from STATSnetBASE) |u https://ezproxy.msu.edu/login?url=https://www.taylorfrancis.com/books/9781003205685 |z STATSnetBASE: 2021 |