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  • Book Overview & Buying Data Analysis with R, Second Edition
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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition - Second Edition

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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition

3.5 (2)

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (19 chapters)
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Random forests


The final classifier that we will be discussing in this chapter is the aptly named Random Forest and is an example of a meta-technique called ensemble learning. The idea and logic behind random forests follows.

Given that (unpruned) decision trees can be nearly bias-less high variance classifiers, a method of reducing variance at the cost of a marginal increase of bias could greatly improve upon the predictive accuracy of the technique. One salient approach to reducing the variance of decision trees is to train a bunch of unpruned decision trees on different random subsets of the training data, sampling with replacement—this is called bootstrap aggregating or bagging. At the classification phase, the test observation is run through all of these trees (a forest, perhaps?), and each resulting classification casts a vote for the final classification of the whole forest. The class with the highest number of votes is the winner. It turns out that the consensus among many high-variance...

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Data Analysis with R, Second Edition
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