Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Table of Contents (19 chapters)
R for Data Science Cookbook
About the Author
About the Reviewer

Chapter 11. Supervised Machine Learning

This chapter covers the following topics:

  • Fitting a linear regression model with lm

  • Summarizing linear model fits

  • Using linear regression to predict unknown values

  • Measuring the performance of the regression model

  • Performing a multiple regression analysis

  • Selecting the best-fitted regression model with stepwise regression

  • Applying the Gaussian model for generalized linear regression

  • Performing a logistic regression analysis

  • Building a classification model with recursive partitioning trees

  • Visualizing a recursive partitioning tree

  • Measuring model performance with a confusion matrix

  • Measuring prediction performance using ROCR