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Index
Title Page
Copyright and Credits
Machine Learning for Data Mining
Contributors
About the author
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
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Introducing Machine Learning Predictive Models
Characteristics of machine learning predictive models
Types of machine learning predictive models
Working with neural networks
Advantages of neural networks
Disadvantages of neural networks
Representing the errors
Types of neural network models
Multi-layer perceptron
Why are weights important?
An example representation of a multilayer perceptron model
The linear regression model
A sample neural network model
Feed-forward backpropagation
Model training ethics
Summary
Getting Started with Machine Learning
Demonstrating a neural network
Running a neural network model
Interpreting results
Analyzing the accuracy of the model
Model performance on testing partition
Support Vector Machines
Working with Support Vector Machines
Kernel transformation
But what is the best solution?
Types of kernel functions
Demonstrating SVMs
Interpreting the results
Trying additional solutions
Summary
Understanding Models
Models
Statistical models
Decision tree models
Machine learning models
Using graphs to interpret machine learning models
Using statistics to interpret machine learning models
Understanding the relationship between a continuous predictor and a categorical outcome variable
Using decision trees to interpret machine learning models
Summary
Improving Individual Models
Modifying model options
Using a different model to improve results
Removing noise to improve models
How to remove noise
Doing additional data preparation
Preparing the data
Balancing data
The need for balancing data
Implementing balance in data
Summary
Advanced Ways of Improving Models
Combining models
Combining by voting
Combining by highest confidence
Implementing combining models
Combining models in Modeler
Combining models outside Modeler
Using propensity scores
Implementations of propensity scores
Meta-level modeling
Error modeling
Boosting and bagging
Boosting
Bagging
Predicting continuous outcomes
Summary
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