Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
About the Author
About the Reviewers
Packt Upsell
Customer Feedback

Business and data understanding

We are once again going to visit our wine data set that we used in Chapter 8, Cluster Analysis. If you recall, it consists of 13 numeric features and a response of three possible classes of wine. Our task is to predict those classes. I will include one interesting twist and that is to artificially increase the number of observations. The reasons are twofold. First, I want to fully demonstrate the resampling capabilities of the mlr package, and second, I wish to cover a synthetic sampling technique. We utilized upsampling in the prior section, so synthetic is in order.

Our first task is to load the package libraries and bring the data:

    > library(mlr)

    > library(ggplot2)

    > library(HDclassif)

    > library(DMwR)

    > library(reshape2)

    > library(corrplot)

    > data(wine)

    > table(wine$class)

     1  2  3 
    59 71 48

We have 178 observations, plus the response labels are numeric (1, 2 and 3). Let's more than double...