Machine Learning with R电子书

Machine Learning with R

Table of Contents

Machine Learning with R

Credits

About the Author

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

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

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

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

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

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

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 – the correlation matrix

Visualizing relationships among features – the 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

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

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 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

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

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

The 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

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

12. Specialized Machine Learning Topics

Working with specialized data

Getting data from the Web with the RCurl package

Reading and writing XML with the XML package

Reading and writing JSON with the rjson package

Reading and writing Microsoft Excel spreadsheets using xlsx

Working with bioinformatics data

Working with social network data and 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