Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

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 (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Choosing a classifier


Note

The following figures my be indecipherable if viewed in black and white. Color versions of these figures can be found on Packt's website. Please make sure you seek these out for a full understanding of the material!

These are just four of the most popular classifiers out there, but there are many more to choose from. Although some classification mechanisms perform better on some types of datasets than others, it can be hard to develop intuition as to exactly the ones they are suitable for. In order to help with this, we will be examining the efficacy of our four classifiers on four different two-dimensional made-up datasets, each with a vastly different optimal decision boundary. In doing so, we will learn more about the characteristics of each classifier and gain a better sense of the kinds of data they might be better suited for.

The four datasets are depicted in Figure 10.11:

Figure 10.11: A plot depicting the class patterns of our four illustrative and contrived...