Learning Apache Mahout Classification

Build and personalize your own classifiers using Apache Mahout

About This Book

• Explore the different types of classification algorithms available in Apache Mahout
• Create and evaluate your own ready-to-use classification models using real world datasets
• A practical guide to problems faced in classification with concepts explained in an easy-to-understand manner

Who This Book Is For

If you are a data scientist who has some experience with the Hadoop ecosystem and machine learning methods and want to try out classification on large datasets using Mahout, this book is ideal for you. Knowledge of Java is essential.

In Detail

This book is a practical guide that explains the classification algorithms provided in Apache Mahout with the help of actual examples. Starting with the introduction of classification and model evaluation techniques, we will explore Apache Mahout and learn why it is a good choice for classification.

Next, you will learn about different classification algorithms and models such as the Naive Bayes algorithm, the Hidden Markov Model, and so on.

Finally, along with the examples that assist you in the creation of models, this book helps you to build a mail classification system that can be produced as soon as it is developed. After reading this book, you will be able to understand the concept of classification and the various algorithms along with the art of building your own classifiers.

Table of Contents

Chapter 1. Classification in Data Analysis
Chapter 2. Apache Mahout
Chapter 3. Learning Logistic Regression / SGD Using Mahout
Chapter 4. Learning the Naïve Bayes Classification Using Mahout
Chapter 5. Learning the Hidden Markov Model Using Mahout
Chapter 6. Learning Random Forest Using Mahout
Chapter 7. Learning Multilayer Perceptron Using Mahout
Chapter 8. Mahout Changes in the Upcoming Release
Chapter 9. Building an E-mail Classification System Using Apache Mahout