Machine Learning for Knowledge Discovery with R (e-bog) af Tsai, Kao-Tai
Tsai, Kao-Tai (forfatter)

Machine Learning for Knowledge Discovery with R e-bog

403,64 DKK (inkl. moms 504,55 DKK)
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...
E-bog 403,64 DKK
Forfattere Tsai, Kao-Tai (forfatter)
Udgivet 14 september 2021
Længde 244 sider
Genrer KCHS
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9781000450279
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.