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
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
16
Sources

Modeling and evaluation


Having created our data frame, df, we can begin to develop the clustering algorithms. We will start with hierarchical and then try our hand at k-means. After this, we will need to manipulate our data a little bit to demonstrate how to incorporate mixed data with Gower and Random Forest.

Hierarchical clustering

To build a hierarchical cluster model in R, you can utilize the hclust() function in the base stats package. The two primary inputs needed for the function are a distance matrix and the clustering method. The distance matrix is easily done with the dist() function. For the distance, we will use Euclidean distance. A number of clustering methods are available, and the default for hclust() is the complete linkage.We will try this, but I also recommend Ward's linkage method. Ward's method tends to produce clusters with a similar number of observations.

The complete linkage method results in the distance between any two clusters that is the maximum distance between...