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

Algorithm flowchart

The purpose of this section is to create a tool that will help you not just select possible modeling techniques but also think deeper about the problem. The residual benefit is that it may help you frame the problem with the project sponsor/team. The techniques in the flowchart are certainly not comprehensive but are exhaustive enough to get you started. It also includes techniques not discussed in this book.

The following figure starts the flow of selecting the potential modeling techniques. As you answer the question(s), it will take you to one of the four additional charts:

Figure 2

If the data is text or in the time series format, then you will follow the flow in the following figure:

Figure 3

In this branch of the algorithm, you do not have text or time series data. You also do not want to predict a category, so you are looking to make recommendations, understand associations, or predict a quantity:

Figure 4

To get to this section, you will have data that is not text or time series. You want to categorize the data, but it does not have an outcome label, which brings us to clustering methods, as follows:

Figure 5

This brings us to the situation where we want to categorize the data and it is labeled, that is, classification:

Figure 6