Learning Apache Mahout Classification: Build and personalize your own classifiers using Apache Mahout

218 Pages·2015·4.672 MB·English

by  Ashish Gupta

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Learning Apache Mahout Classification
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Table of Contents
Learning Apache Mahout Classification
Credits
About the Author
About the Reviewers
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Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Classification in Data Analysis
Introducing the classification
Application of the classification system
Working of the classification system
Classification algorithms
Model evaluation techniques
The confusion matrix
The Receiver Operating Characteristics (ROC) graph
Area under the ROC curve
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The entropy matrix
Summary
2. Apache Mahout
Introducing Apache Mahout
Algorithms supported in Mahout
Reasons for Mahout being a good choice for classification
Installing Mahout
Building Mahout from source using Maven
Installing Maven
Building Mahout code
Setting up a development environment using Eclipse
Setting up Mahout for a Windows user
Summary
3. Learning Logistic Regression / SGD Using Mahout
Introducing regression
Understanding linear regression
Cost function
Gradient descent
Logistic regression
Stochastic Gradient Descent
Using Mahout for logistic regression
Summary
4. Learning the Naïve Bayes Classification Using Mahout
Introducing conditional probability and the Bayes rule
Understanding the Naïve Bayes algorithm
Understanding the terms used in text classification
Using the Naïve Bayes algorithm in Apache Mahout
Summary
5. Learning the Hidden Markov Model Using Mahout
Deterministic and nondeterministic patterns
The Markov process
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Introducing the Hidden Markov Model
Using Mahout for the Hidden Markov Model
Summary
6. Learning Random Forest Using Mahout
Decision tree
Random forest
Using Mahout for Random forest
Steps to use the Random forest algorithm in Mahout
Summary
7. Learning Multilayer Perceptron Using Mahout
Neural network and neurons
Multilayer Perceptron
MLP implementation in Mahout
Using Mahout for MLP
Steps to use the MLP algorithm in Mahout
Summary
8. Mahout Changes in the Upcoming Release
Mahout new changes
Mahout Scala and Spark bindings
Apache Spark
Using Mahout’s Spark shell
H2O platform integration
Summary
9. Building an E-mail Classification System Using Apache Mahout
Spam e-mail dataset
Creating the model using the Assassin dataset
Program to use a classifier model
Testing the program
Second use case as an exercise
The ASF e-mail dataset
Classifiers tuning
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Summary
Index
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Learning Apache Mahout Classification
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