Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data

Chapter 7. Decision Trees

Decision trees are one of the simplest (and most popular) of machine learning algorithms, yet they are extremely powerful and used extensively. If you have used a flowchart before, then understanding a decision tree won't be at all difficult for you. A decision tree is a flowchart except in this case, the machine learning algorithm builds this flowchart, for you. Based on the input data, the decision tree algorithm automatically internally creates a knowledge base of a set of rules based on which it can predict an outcome when given a new set of data. In this chapter, we will cover the following topics:

  • Concepts of a decision tree machine learning classifier, including what a decision tree is, how it is built, and how it can be improved

  • The uses of the decision tree

  • A sample case study using decision trees for classification

Let's try to understand the basics of decision trees now.