Build and personalize your own classifiers using Apache Mahout
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 Naïve 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.
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
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