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MachineLearningwithR.pdf 英文原版
Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
Ethical considerations
How do machines learn?
Abstraction and knowledge representation
Generalization
Assessing the success of learning
Steps to apply machine learning
to your data
Choosing a machine learning algorithm
Thinking about the input data
Thinking about types of machine learning algorithms
Matching your data to an appropriate algorithm
Using R for machine learning
Installing and loading R packages
Installing an R package
Installing a package using the point-and-click interface
Loading an R package
Summary
Chapter 2: Managing and
Understanding Data
R data structures
Vectors
Factors
Lists
Data frames
Matrixes and arrays
Managing data with R
Saving and loading R data structures
Importing and saving data from CSV files
Importing data from SQL databases
Exploring and understanding data
Exploring the structure of data
Exploring numeric variables
Measuring the central tendency – mean and median
Measuring spread – quartiles and the
five-number summary
Visualizing numeric variables – boxplots
Visualizing numeric variables – histograms
Understanding numeric data – uniform and
normal distributions
Measuring spread – variance and standard deviation
Exploring categorical variables
Measuring the central tendency – the mode
Exploring relationships between variables
Visualizing relationships – scatterplots
Examining relationships – two-way
cross-tabulations
Summary
Chapter 3: Lazy Learning – Classification
using Nearest Neighbors
Understanding classification using nearest neighbors
The kNN algorithm
Calculating distance
Choosing an appropriate k
Preparing data for use with kNN
Why is the kNN algorithm lazy?
Diagnosing breast cancer with the
kNN algorithm
Step 1 – collecting data
Step 2 – exploring and preparing the data
Transformation – normalizing numeric data
Data preparation – creating training and
test datasets
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Transformation – z-score standardization
Testing alternative values of k
Summary
Chapter 4: Probabilistic
Learning – Classification using Naive Bayes
Understanding naive Bayes
Basic concepts of Bayesian methods
Probability
Joint probability
Conditional probability with Bayes' theorem
The naive Bayes algorithm
The naive Bayes classification
The Laplace estimator
Using numeric features with naive Bayes
Example – filtering mobile phone spam with the naive Bayes algorithm
Step 1 – collecting data
Step 2 – exploring and preparing the data
Data preparation – processing text data
for analysis
Data preparation – creating training and
test datasets
Visualizing text data – word clouds
Data preparation – creating indicator features for frequent words
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Summary
Chapter 5: Divide and
Conquer – Classification using Decision Trees
and Rules
Understanding decision trees
Divide-and-conquer
The C5.0 decision tree algorithm
Choosing the best split
Pruning the decision tree
Example – identifying risky bank loans using C5.0 decision trees
Step 1 – collecting data
Step 2 – exploring and preparing the data
Data preparation – creating random training and test datasets
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Boosting the accuracy of decision trees
Making some mistakes more costly than others
Understanding classification rules
Separate-and-conquer
The One Rule algorithm
The RIPPER algorithm
Rules from decision trees
Example – identifying poisonous mushrooms with rule learners
Step 1 – collecting data
Step 2 – exploring and preparing the data
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Summary
Chapter 6: Forecasting Numeric
Data – Regression Methods
Understanding regression
Simple linear regression
Ordinary least squares estimation
Correlations
Multiple linear regression
Example – predicting medical expenses using linear regression
Step 1 – collecting data
Step 2 – exploring and preparing the data
Exploring relationships among
features – correlation matrix
Visualizing relationships among
features – scatterplot matrix
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Model specification – adding non-linear relationships
Transformation – converting a numeric variable to
a binary indicator
Model specification – adding interaction effects
Putting it all together – an improved regression model
Understanding regression trees and model trees
Adding regression to trees
Example – estimating the quality of wines with regression trees and model trees
Step 1 – collecting data
Step 2 – exploring and preparing the data
Step 3 – training a model on the data
Visualizing decision trees
Step 4 – evaluating model performance
Measuring performance with mean absolute error
Step 5 – improving model performance
Summary
Chapter 7: Black Box Methods – Neural Networks and Support
Vector Machines
Understanding neural networks
From biological to artificial neurons
Activation functions
Network topology
The number of layers
The direction of information travel
The number of nodes in each layer
Training neural networks with backpropagation
Modeling the strength of concrete
with ANNs
Step 1 – collecting data
Step 2 – exploring and preparing the data
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Understanding Support Vector Machines
Classification with hyperplanes
Finding the maximum margin
The case of linearly separable data
The case of non-linearly separable data
Using kernels for non-linear spaces
Performing OCR with SVMs
Step 1 – collecting data
Step 2 – exploring and preparing the data
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Summary
Chapter 8: Finding Patterns – Market Basket Analysis using Association Rules
Understanding association rules
The Apriori algorithm for association
rule learning
Measuring rule interest – support and confidence
Building a set of rules with the Apriori principle
Example – identifying frequently purchased groceries with
association rules
Step 1 – collecting data
Step 2 – exploring and preparing the data
Data preparation – creating a sparse matrix for transaction data
Visualizing item support – item frequency plots
Visualizing the transaction data – plotting the sparse matrix
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Sorting the set of association rules
Taking subsets of association rules
Saving association rules to a file or data frame
Summary
Chapter 9: Finding Groups of
Data – Clustering
with k-means
Understanding clustering
Clustering as a machine learning task
The k-means algorithm for clustering
Using distance to assign and update clusters
Choosing the appropriate number of clusters
Finding teen market segments using
k-means clustering
Step 1 – collecting data
Step 2 – exploring and preparing the data
Data preparation – dummy coding missing values
Data preparation – imputing missing values
Step 3 – training a model on the data
Step 4 – evaluating model performance
Step 5 – improving model performance
Summary
Chapter 10: Evaluating Model Performance
Measuring performance for classification
Working with classification prediction data in R
A closer look at confusion matrices
Using confusion matrices to measure performance
Beyond accuracy – other measures of performance
Kappa statistic
Sensitivity and specificity
Precision and recall
The F-measure
Visualizing performance tradeoffs
ROC curves
Estimating future performance
The holdout method
Cross-validation
Bootstrap sampling
Summary
Chapter 11: Improving Model Performance
Tuning stock models for better performance
Using caret for automated parameter tuning
Creating a simple tuned model
Customizing the tuning process
Improving model performance with
meta-learning
Understanding ensembles
Bagging
Boosting
Random forests
Training random forests
Evaluating random forest performance
Summary
Chapter 12: Specialized Machine Learning Topics
Working with specialized data
Getting data from the Web with RCurl
Reading and writing XML with 'XML'
Reading and writing JSON with rjson
Reading and writing Microsoft Excel spreadsheets using xlsx
Working with bioinformatics data
Working with social network or graph data
Improving the performance of R
Managing very large datasets
Making data frames faster with data.table
Creating disk-based data frames with ff
Using massive matrices with bigmemory
Learning faster with parallel computing
Measuring execution time
Working in parallel with foreach
Using a multitasking operating system
with multicore
Networking multiple workstations with snow
and snowfall
Parallel cloud computing with MapReduce
and Hadoop
GPU computing
Deploying optimized learning algorithms
Building bigger regression models with biglm
Growing bigger and faster random forests
with bigrf
Training and evaluating models in parallel
with caret
Summary
Index
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Machine Learning with R
Learn how to use R to apply powerful machine learning
methods and gain an insight into real-world applications
Brett Lantz
BIRMINGHAM - MUMBAI
Machine Learning with R
Copyright © 2013 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval
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critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy
of the information presented. However, the information contained in this book is
sold without warranty, either express or implied. Neither the author, nor Packt
Publishing, and its dealers and distributors will be held liable for any damages
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However, Packt Publishing cannot guarantee the accuracy of this information.
First published: October 2013
Production Reference: 1211013
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ISBN 978-1-78216-214-8
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Cover Image by Abhishek Pandey (abhishek.pandey1210@gmail.com)
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Credits
Author
Brett Lantz
Reviewers
Jia Liu
Mzabalazo Z. Ngwenya
Abhinav Upadhyay
Acquisition Editor
James Jones
Lead Technical Editor
Azharuddin Sheikh
Technical Editors
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Harshad Vairat
Project Coordinator
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Proofreaders
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Indexer
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Graphics
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Production Coordinator
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Cover Work
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About the Author
Brett Lantz has spent the past 10 years using innovative data methods to
understand human behavior. A sociologist by training, he was first enchanted by
machine learning while studying a large database of teenagers' social networking
website profiles. Since then, he has worked on interdisciplinary studies of cellular
telephone calls, medical billing data, and philanthropic activity, among others. When
he's not spending time with family, following college sports, or being entertained by
his dachshunds, he maintains dataspelunking.com, a website dedicated to sharing
knowledge about the search for insight in data.
This book could not have been written without the support of my
family and friends. In particular, my wife Jessica deserves many
thanks for her patience and encouragement throughout the past
year. My son Will (who was born while Chapter 10 was underway),
also deserves special mention for his role in the writing process;
without his gracious ability to sleep through the night, I could
not have strung together a coherent sentence the next morning. I
dedicate this book to him in the hope that one day he is inspired to
follow his curiosity wherever it may lead.
I am also indebted to many others who supported this book
indirectly. My interactions with educators, peers, and collaborators
at the University of Michigan, the University of Notre Dame, and the
University of Central Florida seeded many of the ideas I attempted
to express in the text. Additionally, without the work of researchers
who shared their expertise in publications, lectures, and source code,
this book might not exist at all. Finally, I appreciate the efforts of the
R team and all those who have contributed to R packages, whose
work ultimately brought machine learning to the masses.
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About the Reviewers
Jia Liu holds a Master's degree in Statistics from the University of Maryland,
Baltimore County, and is presently a PhD candidate in statistics from Iowa State
University. Her research interests include mixed-effects model, Bayesian method,
Boostrap method, reliability, design of experiments, machine learning and data
mining. She has two year's experience as a student consultant in statistics and two
year's internship experience in agriculture and pharmaceutical industry.
Mzabalazo Z. Ngwenya has worked extensively in the field of statistical
consulting and currently works as a biometrician. He holds an MSc in Mathematical
Statistics from the University of Cape Town and is at present studying for a PhD
(at the School of Information Technology, University of Pretoria), in the field of
Computational Intelligence. His research interests include statistical computing,
machine learning, and spatial statistics. Previously, he was involved in reviewing
Learning RStudio for R Statistical Computing (Van de Loo and de Jong, 2012), and
R Statistical Application Development by Example beginner's guide (Prabhanjan
Narayanachar Tattar , 2013).
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Abhinav Upadhyay finished his Bachelor's degree in 2011 with a major in
Information Technology. His main areas of interest include machine learning and
information retrieval.
In 2011, he worked for the NetBSD Foundation as part of the Google Summer of
Code program. During that period, he wrote a search engine for Unix manual pages.
This project resulted in a new implementation of the apropos utility for NetBSD.
Currently, he is working as a Development Engineer for SocialTwist. His day-to-day
work involves writing system level tools and frameworks to manage the product
infrastructure.
He is also an open source enthusiast and quite active in the community. In his free
time, he maintains and contributes to several open source projects.
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