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Machine Learning with R

Machine Learning with R

By : Brett Lantz
4.2 (46)
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Machine Learning with R

Machine Learning with R

4.2 (46)
By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Table of Contents (16 chapters)
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Preface
Preface
Who this book is for
What this book covers
What you need for this book
Get in touch
Free Chapter
1
1. Introducing Machine Learning
1. 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
2. Managing and Understanding Data
2. Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
3
3. Lazy Learning – Classification Using Nearest Neighbors
3. Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
4
4. Probabilistic Learning – Classification Using Naive Bayes
4. Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
5
5. Divide and Conquer – Classification Using Decision Trees and Rules
5. 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
6. Forecasting Numeric Data – Regression Methods
6. 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
7. Black Box Methods – Neural Networks and Support Vector Machines
7. 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
8. Finding Patterns – Market Basket Analysis Using Association Rules
8. Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
9
9. Finding Groups of Data – Clustering with k-means
9. Finding Groups of Data – Clustering with k-means
Understanding clustering
Finding teen market segments using k-means clustering
Summary
10
10. Evaluating Model Performance
10. Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
11
11. Improving Model Performance
11. Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
12
12. Specialized Machine Learning Topics
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12. Specialized Machine Learning Topics
Managing and preparing real-world data
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary
13
Other Books You May Enjoy
Other Books You May Enjoy
14
Leave a review - let other readers know what you think
Leave a review - let other readers know what you think
15
Index
Index

Improving the performance of R

Base R has a reputation for being slow and memory inefficient, a reputation that is at least somewhat earned. These faults are largely unnoticed on a modern PC for datasets of many thousands of records, but datasets with a million records or more can exceed the limits of what is currently possible with consumer-grade hardware. The problem is worsened if the dataset contains many features or if complex learning algorithms are being used.

Note

CRAN has a high-performance computing task view that lists packages pushing the boundaries on what is possible in R at http://cran.r-project.org/web/views/HighPerformanceComputing.html.

Packages that extend R past the capabilities of the base package are being developed rapidly. This work comes primarily on two fronts: some packages add the capability to manage extremely large datasets by making data operations faster or by allowing the size of data to exceed the amount of available system memory; others allow R to work faster...

End of Section 1
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