Book Image

Java Data Analysis

By : John R. Hubbard
Book Image

Java Data Analysis

By: John R. Hubbard

Overview of this book

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Table of Contents (20 chapters)
Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Logistic regression


A classification algorithm is a process whose input is a training set, as previously described, and whose output is a function that classifies data points. The ID3 algorithm produces a decision tree for the classification function. The naive Bayes algorithm produces a function that classifies by computing ratios from the training set. The SVM algorithm produces an equation of a hyperplane (or hypersurface) that classifies a point by computing on which side of the hyperplane the point lies.

In all three of these algorithms, we assumed that all the attributes of the training set were nominal. If the attributes are instead numeric, we can apply linear regression, as we did in Chapter 6, Regression Analysis. The idea of logistic regression is to transform a problem whose target attribute is Boolean (that is, its value is either 0 or 1) into a numeric variable, run linear regression on that transformed problem, and then transform the solution back into the terms of the given...