book

Machine Learning with R - Second Edition

Name: Machine Learning with R - Second Edition
Author: Brett Lantz
ISBN: 9781784393908

by Brett Lantz

July 2015

Beginner to intermediate

452 pages

11h 33m

English

Packt Publishing

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Machine Learning with R Second Edition
Table of Contents
Machine Learning with R Second Edition
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 codeDownloading the color images of this bookErrataPiracyQuestions
1. Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
Machine learning successesThe limits of machine learningMachine learning ethics
How machines learn
Data storageAbstractionGeneralizationEvaluation
Machine learning in practice
Types of input dataTypes of machine learning algorithmsMatching input data to algorithms
Machine learning with R
Installing R packagesLoading and unloading R packages
Summary
2. Managing and Understanding Data
R data structuresVectorsFactorsListsData framesMatrixes and arrays
Managing data with R
Saving, loading, and removing R data structuresImporting and saving data from CSV files
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 nearest neighbor classificationThe k-NN algorithmMeasuring similarity with distanceChoosing an appropriate kPreparing data for use with k-NNWhy is the k-NN algorithm lazy?
Example – diagnosing breast cancer with the k-NN 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 methodsUnderstanding probabilityUnderstanding joint probabilityComputing conditional probability with Bayes' theoremThe Naive Bayes algorithmClassification with Naive BayesThe 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 – cleaning and standardizing text dataData preparation – splitting text documents into wordsData 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 mistakes more costlier than others
Understanding classification rules
Separate and conquerThe 1R algorithmThe RIPPER algorithmRules from decision treesWhat makes trees and rules greedy?
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 the 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
Example – 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 hyperplanesThe case of linearly separable dataThe case of nonlinearly separable dataUsing kernels for non-linear spaces
Example – 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 the 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 clustering algorithmUsing distance to assign and update clustersChoosing the appropriate number of clusters
Example – finding teen market segments using k-means clustering
Step 1 – collecting dataStep 2 – exploring and preparing the dataData preparation – dummy coding missing valuesData preparation – imputing the 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 trade-offsROC 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 proprietary files and databasesReading from and writing to Microsoft Excel, SAS, SPSS, and Stata filesQuerying data in SQL databases
Working with online data and services
Downloading the complete text of web pagesScraping data from web pagesParsing XML documentsParsing JSON from web APIs
Working with domain-specific data
Analyzing bioinformatics dataAnalyzing and visualizing network data
Improving the performance of R
Managing very large datasetsGeneralizing tabular data structures with dplyrMaking 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 multicore and snowTaking advantage of parallel with foreach and doParallelParallel 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

Machine Learning with R (Second Edition) provides a thorough introduction to machine learning techniques and their application using the R programming language. You'll gain hands-on experience implementing various algorithms and solving real-world data challenges, making it an invaluable resource for aspiring data scientists and analysts.

What this Book will help me do

Understand the fundamentals of machine learning and its applications in data analysis.
Master the use of R for cleaning, exploring, and visualizing data to prepare it for modeling.
Build and apply machine learning models for classification, prediction, and clustering tasks.
Evaluate and fine-tune model performance to ensure accurate predictions.
Explore advanced topics like text mining, handling social network data, and big data analytics.

Author(s)

Brett Lantz is a data scientist with significant experience as both a practitioner and communicator in the machine learning field. With a focus on accessibility, he aims to demystify complex concepts for readers interested in data science. His blend of hands-on methods and theoretical insight has made his work a favorite for both beginners and experienced professionals.

Who is it for?

Ideal for data analysts and aspiring data scientists who have intermediate programming skills and are exploring machine learning. Perfect for R users ready to expand their skill set to include predictive modeling techniques. Also fits those with some experience in machine learning but new to the R environment. Provides insightful guidance for anyone looking to apply machine learning in practical, real-world scenarios.

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