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


In this chapter, we covered a very important and popular algorithm in machine learning called as decision trees. A decision tree is very similar to a flowchart and is based on a set of rules. A decision tree algorithm learns from a dataset and builds a set of rules. Based on these rules, it splits the dataset into two (in the case of binary splits) or more parts. When a new data is fed in for predictions based on the attributes of the data, a particular path is taken and this follows along the full path of rules in the tree until a particular response is reached.

There are many ways in which we can split data in a decision tree. We explored two of the most common ways called Entropy and Gini Impurity. In either of these cases, the main criteria is to use the split mechanism, which makes the split set as homogeneous as possible. Both Entropy and Gini Impurity are mathematical formulas or approaches and as such the entire model works on numerical data.

In the next chapter, we will learn...