Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

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

Machine Learning with R Cookbook, Second Edition - Second Edition

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.

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Table of Contents (21 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
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
Understanding of basic data structures
Basic commands for subsetting
Reading and writing data
Manipulating data
Applying basic statistics
Visualizing data
Getting a dataset for machine learning
2
Data Exploration with Air Quality Datasets
Data Exploration with Air Quality Datasets
Introduction
Using air quality dataset
Converting attributes to factor
Detecting missing values
Imputing missing values
Exploring and visualizing data
Predicting values from datasets
3
Analyzing Time Series Data
Analyzing Time Series Data
Introduction
Looking at time series data
Plotting and forecasting time series data
Extracting, subsetting, merging, filling, and padding
Successive differences and moving averages
Exponential smoothing
Plotting the autocorrelation function
4
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
Conducting an exact binomial test
Performing a 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
5
Understanding Regression Analysis
Understanding Regression Analysis
Introduction
Different types of regression
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 multiple regression
Summarizing multiple regression
Using multiple regression to predict unknown values
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
6
Survival Analysis
Survival Analysis
Introduction
Loading and observing data
Viewing the summary of survival analysis
Visualizing the Survival Curve
Using the log-rank test
Using the COX proportional hazard model
Nelson-Aalen Estimator of cumulative hazard
7
Classification 1 - Tree, Lazy, and Probabilistic
Classification 1 - 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
Handling missing data and split and surrogate variables
Building a classification model with a conditional inference tree
Control parameters in conditional inference trees
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
8
Classification 2 - Neural Network and SVM
Classification 2 - 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
The basics of neural network
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
9
Model Evaluation
Model Evaluation
Introduction
Estimating model performance with k-fold cross-validation
Estimating model performance with Leave One Out 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
10
Ensemble Learning
Ensemble Learning
Introduction
Using the Super Learner algorithm
Using ensemble to train and test
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
11
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
12
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
Using the TraMineR package for sequence analysis
Visualizing sequence, Chronogram, and Traversal Statistics
13
Dimension Reduction
Dimension Reduction
Introduction
Why to reduce the dimension?
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
14
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

Obtaining the optimum number of clusters for k-means

While k-means clustering is fast and easy to use, it requires k to be the input at the beginning. Therefore, we can use the sum of squares to determine which k value is best for finding the optimum number of clusters for k-means. In the following recipe, we will discuss how to find the optimum number of clusters for the k-means clustering method.

Getting ready

In order to find the optimum number of clusters, you need to have the previous recipe completed by generating the customer dataset.

How to do it...

Perform the following steps to find the optimum number of clusters for the k-means clustering:

  1. First, calculate the within sum of squares (withinss) of different numbers of clusters:
        > nk = 2:10
        > set.seed(22)
        > WSS = sapply(nk, function(k) {
        +     kmeans(customer, centers=k)$tot.withinss
        + })
        > WSS
        Output
        [1] 123.49224  88.07028  61.34890  48.76431  47.20813
     ...
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