book

Machine Learning with R

Name: Machine Learning with R
Author: Brett Lantz
ISBN: 9781782162148

by Brett Lantz

October 2013

Beginner to intermediate

396 pages

10h 18m

English

Packt Publishing

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Machine Learning with R
Table of Contents
Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
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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 codeErrataPiracyQuestions
1. Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
Ethical considerations
How do machines learn?
Abstraction and knowledge representationGeneralizationAssessing the success of learning
Steps to apply machine learning to your data
Choosing a machine learning algorithm
Thinking about the input dataThinking about types of machine learning algorithmsMatching your data to an appropriate algorithm
Using R for machine learning
Installing and loading R packagesInstalling an R packageInstalling a package using the point-and-click interfaceLoading an R package
Summary
2. Managing and Understanding Data
R data structures
Vectors
Factors
ListsData framesMatrixes and arrays
Managing data with R
Saving and loading R data structuresImporting and saving data from CSV filesImporting data from SQL databases
Exploring and understanding data
Exploring the structure of dataExploring numeric variablesMeasuring the central tendency – mean and medianMeasuring spread – quartiles and the five-number summaryVisualizing numeric variables – boxplotsVisualizing numeric variables – histogramsUnderstanding numeric data – uniform and normal distributionsMeasuring spread – variance and standard deviationExploring categorical variablesMeasuring the central tendency – the modeExploring relationships between variablesVisualizing relationships – scatterplotsExamining relationships – two-way cross-tabulations
Summary
3. Lazy Learning – Classification Using Nearest Neighbors
Understanding classification using nearest neighborsThe kNN algorithmCalculating distanceChoosing an appropriate kPreparing data for use with kNNWhy is the kNN algorithm lazy?
Diagnosing breast cancer with the kNN algorithm
Step 1 – collecting dataStep 2 – exploring and preparing the dataTransformation – normalizing numeric dataData preparation – creating training and test datasetsStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performanceTransformation – z-score standardizationTesting alternative values of k
Summary
4. Probabilistic Learning – Classification Using Naive Bayes
Understanding naive BayesBasic concepts of Bayesian methodsProbabilityJoint probabilityConditional probability with Bayes' theoremThe naive Bayes algorithmThe naive Bayes classificationThe Laplace estimatorUsing numeric features with naive Bayes
Example – filtering mobile phone spam with the naive Bayes algorithm
Step 1 – collecting dataStep 2 – exploring and preparing the dataData preparation – processing text data for analysisData preparation – creating training and test datasetsVisualizing text data – word cloudsData preparation – creating indicator features for frequent wordsStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performance
Summary
5. Divide and Conquer – Classification Using Decision Trees and Rules
Understanding decision treesDivide and conquerThe C5.0 decision tree algorithmChoosing the best splitPruning the decision tree
Example – identifying risky bank loans using C5.0 decision trees
Step 1 – collecting dataStep 2 – exploring and preparing the dataData preparation – creating random training and test datasetsStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performanceBoosting the accuracy of decision treesMaking some mistakes more costly than others
Understanding classification rules
Separate and conquerThe One Rule algorithmThe RIPPER algorithmRules from decision trees
Example – identifying poisonous mushrooms with rule learners
Step 1 – collecting dataStep 2 – exploring and preparing the dataStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performance
Summary
6. Forecasting Numeric Data – Regression Methods
Understanding regressionSimple linear regressionOrdinary least squares estimationCorrelationsMultiple linear regression
Example – predicting medical expenses using linear regression
Step 1 – collecting dataStep 2 – exploring and preparing the dataExploring relationships among features – the correlation matrixVisualizing relationships among features – the scatterplot matrixStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performanceModel specification – adding non-linear relationshipsTransformation – converting a numeric variable to a binary indicatorModel specification – adding interaction effectsPutting 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 dataStep 2 – exploring and preparing the dataStep 3 – training a model on the dataVisualizing decision treesStep 4 – evaluating model performanceMeasuring performance with mean absolute errorStep 5 – improving model performance
Summary
7. Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networksFrom biological to artificial neuronsActivation functionsNetwork topologyThe number of layersThe direction of information travelThe number of nodes in each layerTraining neural networks with backpropagation
Modeling the strength of concrete with ANNs
Step 1 – collecting dataStep 2 – exploring and preparing the dataStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performance
Understanding Support Vector Machines
Classification with hyperplanesFinding the maximum marginThe case of linearly separable dataThe case of non-linearly separable dataUsing kernels for non-linear spaces
Performing OCR with SVMs
Step 1 – collecting dataStep 2 – exploring and preparing the dataStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performance
Summary
8. Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rulesThe Apriori algorithm for association rule learningMeasuring rule interest – support and confidenceBuilding a set of rules with the Apriori principle
Example – identifying frequently purchased groceries with association rules
Step 1 – collecting dataStep 2 – exploring and preparing the dataData preparation – creating a sparse matrix for transaction dataVisualizing item support – item frequency plotsVisualizing transaction data – plotting the sparse matrixStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performanceSorting the set of association rulesTaking subsets of association rulesSaving association rules to a file or data frame
Summary
9. Finding Groups of Data – Clustering with k-means
Understanding clusteringClustering as a machine learning taskThe k-means algorithm for clusteringUsing distance to assign and update clustersChoosing the appropriate number of clustersFinding teen market segments using k-means clusteringStep 1 – collecting dataStep 2 – exploring and preparing the dataData preparation – dummy coding missing valuesData preparation – imputing missing valuesStep 3 – training a model on the dataStep 4 – evaluating model performanceStep 5 – improving model performance
Summary
10. Evaluating Model Performance
Measuring performance for classificationWorking with classification prediction data in RA closer look at confusion matricesUsing confusion matrices to measure performanceBeyond accuracy – other measures of performanceThe kappa statisticSensitivity and specificityPrecision and recallThe F-measureVisualizing performance tradeoffsROC curves
Estimating future performance
The holdout methodCross-validationBootstrap sampling
Summary
11. Improving Model Performance
Tuning stock models for better performanceUsing caret for automated parameter tuningCreating a simple tuned modelCustomizing the tuning process
Improving model performance with meta-learning
Understanding ensemblesBaggingBoostingRandom forestsTraining random forestsEvaluating random forest performance
Summary
12. Specialized Machine Learning Topics
Working with specialized dataGetting data from the Web with the RCurl packageReading and writing XML with the XML packageReading and writing JSON with the rjson packageReading and writing Microsoft Excel spreadsheets using xlsxWorking with bioinformatics dataWorking with social network data and graph data
Improving the performance of R
Managing very large datasetsMaking data frames faster with data.tableCreating disk-based data frames with ffUsing massive matrices with bigmemoryLearning faster with parallel computingMeasuring execution timeWorking in parallel with foreachUsing a multitasking operating system with multicoreNetworking multiple workstations with snow and snowfallParallel cloud computing with MapReduce and HadoopGPU computingDeploying optimized learning algorithmsBuilding bigger regression models with biglmGrowing bigger and faster random forests with bigrfTraining and evaluating models in parallel with caret
Summary
Index

Overview

This book takes you step-by-step through the process of mastering machine learning techniques using R. By exploring practical examples and real-world applications, you will learn how to prepare and analyze data, apply algorithms, and evaluate their results. Whether you're new to machine learning or already familiar with R, this guide will get you up and running quickly.

What this Book will help me do

Understand basic machine learning concepts and terminology.
Learn to prepare and visualize data using R for machine learning.
Apply various machine learning algorithms like classification, regression, and clustering.
Master model evaluation and improvement techniques.
Explore advanced topics like text mining and social network analysis.

Author(s)

Brett Lantz is an experienced data scientist and a skilled R user with years of experience teaching machine learning concepts. With a talent for turning complex subjects into understandable tutorials, Brett has helped countless learners master data science and analysis using R. He emphasizes practical examples and hands-on learning in his writing.

Who is it for?

This book is perfect for data science enthusiasts and practitioners looking to explore machine learning using R. Beginners will find the step-by-step tutorials especially helpful, while those with some experience in R or data analysis can deepen their expertise. Ideal for analysts, data scientists, and anyone interested in extracting actionable insights from data using R.

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