Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data setsĮnsemble Classification Methods with Applications in R by Esteban Alfaro (Editor) Matías Gámez (Editor) Noelia García (Editor) An essential guide to two burgeoning topics in machine learning - classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis.
The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. Manjunath Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. A Course in Statistics with R by Prabhanjan N.
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