Book Image

Mastering Machine Learning with R - Second Edition

Book Image

Mastering Machine Learning with R - 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

The process


The CRISP-DM process was designed specifically for data mining. However, it is flexible and thorough enough to be applied to any analytical project, whether it is predictive analytics, data science, or machine learning. Don't be intimidated by the numerous lists of tasks as you can apply your judgment to the process and adapt it for any real-world situation. The following figure provides a visual representation of the process and shows the feedback loops that make it so flexible:

Figure 1: CRISP-DM 1.0, Step-by-step data mining guide

The process has the following six phases:

  • Business understanding
  • Data understanding
  • Data preparation
  • Modeling
  • Evaluation
  • Deployment

For an in-depth review of the entire process with all of its tasks and subtasks, you can examine the paper by SPSS, CRISP-DM 1.0, step-by-step data mining guide, available at https://the-modeling-agency.com/crisp-dm.pdf.

I will discuss each of the steps in the process, covering the important tasks. However, it will not be in as detailed as the guide, but more high-level. We will not skip any of the critical details but focus more on the techniques that one can apply to the tasks. Keep in mind that these process steps will be used in later chapters as a framework in the actual application of the machine-learning methods in general and the R code, in particular.