Book Image

R Machine Learning By Example

By : Raghav Bali
Book Image

R Machine Learning By Example

By: Raghav Bali

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Table of Contents (15 chapters)
R Machine Learning By Example
About the Authors
About the Reviewer

Modeling using decision trees

Decision trees are algorithms which again belong to the supervised machine learning algorithms family. They are also used for both classification and regression, often called CART, which stands for classification and regression trees. These are used a lot in decision support systems, business intelligence, and operations research.

Decision trees are mainly used for making decisions that would be most useful in reaching some objective and designing a strategy based on these decisions. At the core, a decision tree is just a flowchart with several nodes and conditional edges. Each non-leaf node represents a conditional test on one of the features and each edge represents an outcome of the test. Each leaf node represents a class label where predictions are made for the final outcome. Paths from the root to all the leaf nodes give us all the classification rules. Decision trees are easy to represent, construct, and understand. However, the drawback is that they are...