'Machine Learning for Knowledge Discovery with R' contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes most recent supervised and unsupervised machine learning methodologies such as recursive partitioning tree-based modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes the use of graphical methods for data exploration to understand the internal structure of data sets. The book includes many examples based on real-world big data from life-science, finance, etc. to illustrate the applications of the methods described therein. Key Features: Contains the statistical background theory for the most recent supervised and unsupervised machine learning methodologies such as recursive partitioning tree-based modelling, regression shrinkage, support vector machine, neural network, clustering, and causal-effect analysis, as well as graphical methods. Includes many examples based on real-world big data from life-science, finance, etc. to illustrate the implementation of the methods described therein It is suitable for upper-level-undergraduate or graduate-level course on data analysis. It can also serve as a useful desk-reference for data analysts in scientific research or industrial applications.

Book Information
ISBN 9781032065366
Author Kao-Tai Tsai
Format Hardback
Page Count 264
Imprint Taylor & Francis Ltd
Publisher Taylor & Francis Ltd
Weight(grams) 156g