Book Image

Machine Learning with R Cookbook

By : Yu-Wei, Chiu (David Chiu)
Book Image

Machine Learning with R Cookbook

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

<p>The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.</p> <p>This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.</p>

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Machine Learning with R Cookbook
Yu-Wei, Chiu (David Chiu)
Mar, 2015 | 442 pages
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Table of Contents (21 chapters)
Machine Learning with R Cookbook
Machine Learning with R Cookbook
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
Practical Machine Learning with R
Practical Machine Learning with R
Introduction
Downloading and installing R
Downloading and installing RStudio
Installing and loading packages
Reading and writing data
Using R to manipulate data
Applying basic statistics
Visualizing data
Getting a dataset for machine learning
2
Data Exploration with RMS Titanic
Data Exploration with RMS Titanic
Introduction
Reading a Titanic dataset from a CSV file
Converting types on character variables
Detecting missing values
Imputing missing values
Exploring and visualizing data
Predicting passenger survival with a decision tree
Validating the power of prediction with a confusion matrix
Assessing performance with the ROC curve
3
R and Statistics
R and Statistics
Introduction
Understanding data sampling in R
Operating a probability distribution in R
Working with univariate descriptive statistics in R
Performing correlations and multivariate analysis
Operating linear regression and multivariate analysis
Conducting an exact binomial test
Performing student's t-test
Performing the Kolmogorov-Smirnov test
Understanding the Wilcoxon Rank Sum and Signed Rank test
Working with Pearson's Chi-squared test
Conducting a one-way ANOVA
Performing a two-way ANOVA
4
Understanding Regression Analysis
Understanding Regression Analysis
Introduction
Fitting a linear regression model with lm
Summarizing linear model fits
Using linear regression to predict unknown values
Generating a diagnostic plot of a fitted model
Fitting a polynomial regression model with lm
Fitting a robust linear regression model with rlm
Studying a case of linear regression on SLID data
Applying the Gaussian model for generalized linear regression
Applying the Poisson model for generalized linear regression
Applying the Binomial model for generalized linear regression
Fitting a generalized additive model to data
Visualizing a generalized additive model
Diagnosing a generalized additive model
5
Classification (I) – Tree, Lazy, and Probabilistic
Classification (I) – Tree, Lazy, and Probabilistic
Introduction
Preparing the training and testing datasets
Building a classification model with recursive partitioning trees
Visualizing a recursive partitioning tree
Measuring the prediction performance of a recursive partitioning tree
Pruning a recursive partitioning tree
Building a classification model with a conditional inference tree
Visualizing a conditional inference tree
Measuring the prediction performance of a conditional inference tree
Classifying data with the k-nearest neighbor classifier
Classifying data with logistic regression
Classifying data with the Naïve Bayes classifier
6
Classification (II) – Neural Network and SVM
Classification (II) – Neural Network and SVM
Introduction
Classifying data with a support vector machine
Choosing the cost of a support vector machine
Visualizing an SVM fit
Predicting labels based on a model trained by a support vector machine
Tuning a support vector machine
Training a neural network with neuralnet
Visualizing a neural network trained by neuralnet
Predicting labels based on a model trained by neuralnet
Training a neural network with nnet
Predicting labels based on a model trained by nnet
7
Model Evaluation
Model Evaluation
Introduction
Estimating model performance with k-fold cross-validation
Performing cross-validation with the e1071 package
Performing cross-validation with the caret package
Ranking the variable importance with the caret package
Ranking the variable importance with the rminer package
Finding highly correlated features with the caret package
Selecting features using the caret package
Measuring the performance of the regression model
Measuring prediction performance with a confusion matrix
Measuring prediction performance using ROCR
Comparing an ROC curve using the caret package
Measuring performance differences between models with the caret package
8
Ensemble Learning
Ensemble Learning
Introduction
Classifying data with the bagging method
Performing cross-validation with the bagging method
Classifying data with the boosting method
Performing cross-validation with the boosting method
Classifying data with gradient boosting
Calculating the margins of a classifier
Calculating the error evolution of the ensemble method
Classifying data with random forest
Estimating the prediction errors of different classifiers
9
Clustering
Clustering
Introduction
Clustering data with hierarchical clustering
Cutting trees into clusters
Clustering data with the k-means method
Drawing a bivariate cluster plot
Comparing clustering methods
Extracting silhouette information from clustering
Obtaining the optimum number of clusters for k-means
Clustering data with the density-based method
Clustering data with the model-based method
Visualizing a dissimilarity matrix
Validating clusters externally
10
Association Analysis and Sequence Mining
Association Analysis and Sequence Mining
Introduction
Transforming data into transactions
Displaying transactions and associations
Mining associations with the Apriori rule
Pruning redundant rules
Visualizing association rules
Mining frequent itemsets with Eclat
Creating transactions with temporal information
Mining frequent sequential patterns with cSPADE
11
Dimension Reduction
Dimension Reduction
Introduction
Performing feature selection with FSelector
Performing dimension reduction with PCA
Determining the number of principal components using the scree test
Determining the number of principal components using the Kaiser method
Visualizing multivariate data using biplot
Performing dimension reduction with MDS
Reducing dimensions with SVD
Compressing images with SVD
Performing nonlinear dimension reduction with ISOMAP
Performing nonlinear dimension reduction with Local Linear Embedding
12
Big Data Analysis (R and Hadoop)
Big Data Analysis (R and Hadoop)
Introduction
Preparing the RHadoop environment
Installing rmr2
Installing rhdfs
Operating HDFS with rhdfs
Implementing a word count problem with RHadoop
Comparing the performance between an R MapReduce program and a standard R program
Testing and debugging the rmr2 program
Installing plyrmr
Manipulating data with plyrmr
Conducting machine learning with RHadoop
Configuring RHadoop clusters on Amazon EMR
Resources for R and Machine Learning
Resources for R and Machine Learning
Dataset – Survival of Passengers on the Titanic
Dataset – Survival of Passengers on the Titanic
Index
Index

Introduction

The aim of machine learning is to uncover hidden patterns, unknown correlations, and find useful information from data. In addition to this, through incorporation with data analysis, machine learning can be used to perform predictive analysis. With machine learning, the analysis of business operations and processes is not limited to human scale thinking; machine scale analysis enables businesses to capture hidden values in big data.

Machine learning has similarities to the human reasoning process. Unlike traditional analysis, the generated model cannot evolve as data is accumulated. Machine learning can learn from the data that is processed and analyzed. In other words, the more data that is processed, the more it can learn.

R, as a dialect of GNU-S, is a powerful statistical language that can be used to manipulate and analyze data. Additionally, R provides many machine learning packages and visualization functions, which enable users to analyze data on the fly. Most importantly, R is open source and free.

Using R greatly simplifies machine learning. All you need to know is how each algorithm can solve your problem, and then you can simply use a written package to quickly generate prediction models on data with a few command lines. For example, you can either perform Naïve Bayes for spam mail filtering, conduct k-means clustering for customer segmentation, use linear regression to forecast house prices, or implement a hidden Markov model to predict the stock market, as shown in the following screenshot:

Stock market prediction using R

Moreover, you can perform nonlinear dimension reduction to calculate the dissimilarity of image data, and visualize the clustered graph, as shown in the following screenshot. All you need to do is follow the recipes provided in this book.

A clustered graph of face image data

This chapter serves as an overall introduction to machine learning and R; the first few recipes introduce how to set up the R environment and integrated development environment, RStudio. After setting up the environment, the following recipe introduces package installation and loading. In order to understand how data analysis is practiced using R, the next four recipes cover data read/write, data manipulation, basic statistics, and data visualization using R. The last recipe in the chapter lists useful data sources and resources.

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