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Machine Learning with R - Second Edition

By : Brett Lantz
Book Image

Machine Learning with R - Second Edition

By: Brett Lantz

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Machine Learning with R - Second Edition
Brett Lantz
Jul, 2015 | 452 pages
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Table of Contents (19 chapters)
Machine Learning with R Second Edition
Machine Learning with R Second Edition
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 machines learn
Machine learning in practice
Machine learning with R
Summary
2
Managing and Understanding Data
Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
3
Lazy Learning – Classification Using Nearest Neighbors
Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN 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
Example – Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Example – 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
Example – finding teen market segments using k-means 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 proprietary files and databases
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary
Index
Index

Understanding regression

Regression is concerned with specifying the relationship between a single numeric dependent variable (the value to be predicted) and one or more numeric independent variables (the predictors). As the name implies, the dependent variable depends upon the value of the independent variable or variables. The simplest forms of regression assume that the relationship between the independent and dependent variables follows a straight line.

Note

The origin of the term "regression" to describe the process of fitting lines to data is rooted in a study of genetics by Sir Francis Galton in the late 19th century. He discovered that fathers who were extremely short or extremely tall tended to have sons whose heights were closer to the average height. He called this phenomenon "regression to the mean".

You might recall from basic algebra that lines can be defined in a slope-intercept form similar to y = a + bx. In this form, the letter y indicates the dependent variable and x indicates...

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