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


Modeling will be broken into two distinct parts. The first will focus on word frequency and correlation and culminate in the building of a topic model. In the next portion, we will examine many different quantitative techniques by utilizing the power of the qdap package in order to compare two different speeches.

Word frequency and topic models

As we have everything set up in the document-term matrix, we can move on to exploring word frequencies by creating an object with the column sums, sorted in descending order. It is necessary to use as.matrix() in the code to sum the columns. The default order is ascending, so putting - in front of freq will change it to descending:

> freq <- colSums(as.matrix(dtm))

> ord <- order(-freq)

We will examine the head and tail of the object with the following code:

> freq[head(ord)]
new  america  people   jobs    now  years 
        193      174     168    163    157    148

> freq[tail(ord)]
wright written yearold youngest...