Learning Apache Mahout Classification
- 出版商: Packt Publishing
- 出版日期: 2015-02-27
- 售價: $1,170
- 貴賓價: 9.5 折 $1,112
- 語言: 英文
- 頁數: 130
- 裝訂: Paperback
- ISBN: 1783554959
- ISBN-13: 9781783554959
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.
What You Will Learn
- Apply machine learning techniques in the area of classification
- Categorize the unknown items by using the classification model in Apache Mahout
- Use the classifier to classify text documents
- Implement a multilayer perceptron to map sets of input to appropriate output sets
- Develop the Hidden Markov model for a system with hidden states
- Build and deploy an e-mail classifier that can predict the delivery of incoming mail
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.