Machine Learning with R Cookbook

Machine Learning with R Cookbook

By : Yu-Wei, Chiu (David Chiu)

Buy this Book

Machine Learning with R Cookbook

By: Yu-Wei, Chiu (David Chiu)

Buy this Book

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>

Machine Learning with R Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

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

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

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

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

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

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

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

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

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

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

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

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

Dataset – Survival of Passengers on the Titanic

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Reading and writing data

Before starting to explore data, you must load the data into the R session. This recipe will introduce methods to load data either from a file into the memory or use the predefined data within R.

Getting ready

First, start an R session on your machine. As this recipe involves steps toward the file IO, if the user does not specify the full path, read and write activity will take place in the current working directory.

You can simply type getwd() in the R session to obtain the current working directory location. However, if you would like to change the current working directory, you can use setwd("<path>"), where <path> can be replaced as your desired path, to specify the working directory.

How to do it...

Perform the following steps to read and write data with R:

To view the built-in datasets of R, type the following command:
```
> data()
```
R will return a list of datasets in a dataset package, and the list comprises the name and description of each dataset.
To load the dataset iris into an R session, type the following command:
```
> data(iris)
```
The dataset iris is now loaded into the data frame format, which is a common data structure in R to store a data table.
To view the data type of iris, simply use the class function:
```
> class(iris)
[1] "data.frame"
```
The data.frame console print shows that the iris dataset is in the structure of data frame.
Use the save function to store an object in a file. For example, to save the loaded iris data into myData.RData, use the following command:
```
> save(iris, file="myData.RData")
```
Use the load function to read a saved object into an R session. For example, to load iris data from myData.RData, use the following command:
```
> load("myData.RData")
```
In addition to using built-in datasets, R also provides a function to import data from text into a data frame. For example, the read.table function can format a given text into a data frame:
```
> test.data = read.table(header = TRUE, text = "
+ a b
+ 1 2
+ 3 4
+ ")
```

You can also use row.names and col.names to specify the names of columns and rows:

> test.data = read.table(text = "
+ 1 2
+ 3 4", 
+ col.names=c("a","b"),
+ row.names = c("first","second"))

View the class of the test.data variable:
```
> class(test.data)
[1] "data.frame"
```
The class function shows that the test.data variable contains a data frame.
In addition to importing data by using the read.table function, you can use the write.table function to export data to a text file:
```
> write.table(test.data, file = "test.txt" , sep = " ")
```
The write.table function will write the content of test.data into test.txt (the written path can be found by typing getwd()), with a separation delimiter as white space.
Similar to write.table, write.csv can also export data to a file. However, write.csv uses a comma as the default delimiter:
```
> write.csv(test.data, file = "test.csv")
```
With the read.csv function, the csv file can be imported as a data frame. However, the last example writes column and row names of the data frame to the test.csv file. Therefore, specifying header to TRUE and row names as the first column within the function can ensure the read data frame will not treat the header and the first column as values:
```
> csv.data = read.csv("test.csv", header = TRUE, row.names=1)
> head(csv.data)
  a b
1 1 2
2 3 4
```

How it works

Generally, data for collection may be in multiple files and different formats. To exchange data between files and RData, R provides many built-in functions, such as save, load, read.csv, read.table, write.csv, and write.table.

This example first demonstrates how to load the built-in dataset iris into an R session. The iris dataset is the most famous and commonly used dataset in the field of machine learning. Here, we use the iris dataset as an example. The recipe shows how to save RData and load it with the save and load functions. Furthermore, the example explains how to use read.table, write.table, read.csv, and write.csv to exchange data from files to a data frame. The use of the R IO function to read and write data is very important as most of the data sources are external. Therefore, you have to use these functions to load data into an R session.

Machine Learning with R Cookbook

By : Yu-Wei, Chiu (David Chiu)

Machine Learning with R Cookbook

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

Related Content you might be interested in

Current Title:

Machine Learning with R Cookbook

Reading and writing data

Getting ready

How to do it...

How it works

See also