Book Image

Machine Learning with R

By : Brett Lantz
Book Image

Machine Learning with R

By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.

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Machine Learning with R
Brett Lantz
Oct, 2013 | 396 pages
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Table of Contents (19 chapters)
Machine Learning with R
Machine Learning with R
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introducing Machine Learning
Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How do machines learn?
Steps to apply machine learning to your data
Choosing a machine learning algorithm
Using R for machine learning
Summary
2
Managing and Understanding Data
Managing and Understanding Data
R data structures
Vectors
Factors
Managing data with R
Exploring and understanding data
Summary
3
Lazy Learning – Classification Using Nearest Neighbors
Lazy Learning – Classification Using Nearest Neighbors
Understanding classification using nearest neighbors
Diagnosing breast cancer with the kNN algorithm
Summary
4
Probabilistic Learning – Classification Using Naive Bayes
Probabilistic Learning – Classification Using Naive Bayes
Understanding naive Bayes
Example – filtering mobile phone spam with the naive Bayes algorithm
Summary
5
Divide and Conquer – Classification Using Decision Trees and Rules
Divide and Conquer – Classification Using Decision Trees and Rules
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Summary
6
Forecasting Numeric Data – Regression Methods
Forecasting Numeric Data – Regression Methods
Understanding regression
Example – predicting medical expenses using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Summary
7
Black Box Methods – Neural Networks and Support Vector Machines
Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Performing OCR with SVMs
Summary
8
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
9
Finding Groups of Data – Clustering with k-means
Finding Groups of Data – Clustering with k-means
Understanding clustering
Summary
10
Evaluating Model Performance
Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
11
Improving Model Performance
Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
12
Specialized Machine Learning Topics
Specialized Machine Learning Topics
Working with specialized data
Improving the performance of R
Summary
Index
Index

Understanding regression trees and model trees

If you recall from Chapter 5, Divide and Conquer – Classification Using Decision Trees and Rules, a decision tree builds a model much like a flowchart in which decision nodes, leaf nodes, and branches define a series of decisions that can be used to classify examples. Such trees can also be used for numeric prediction by making only small adjustments to the tree growing algorithm. In this section, we will consider only the ways in which trees for numeric prediction differ from trees used for classification.

Trees for numeric prediction fall into two categories. The first, known as regression trees, were introduced in the 1980s as part of the seminal Classification and Regression Tree (CART) algorithm. Despite the name, regression trees do not use linear regression methods as described earlier in this chapter; rather, they make predictions based on the average value of examples that reach a leaf.

Note

The CART algorithm is described in detail in...

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