book

Machine Learning with R - Third Edition

Name: Machine Learning with R - Third Edition
Author: Brett Lantz
ISBN: 9781788295864

by Brett Lantz

April 2019

Intermediate to advanced

458 pages

12h 35m

English

Packt Publishing

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Machine Learning with R - Third Edition
Table of Contents
Machine Learning with R - Third Edition
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Packt.com
Contributors
About the authors
About the reviewer
Preface
Who this book is for
What this book covers
What you need for this book
Download the example code filesDownload the color imagesConventions used
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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 packagesInstalling RStudio
Summary
2. Managing and Understanding Data
R data structuresVectorsFactorsListsData framesMatrices 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 some mistakes cost more 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 nonlinear relationshipsTransformation – converting a numeric variable to a binary indicatorModel specification – adding interaction effectsPutting it all together – an improved regression modelMaking predictions with a 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 nonlinear 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 performanceChanging the SVM kernel functionIdentifying the best SVM cost parameter
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
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 classificationUnderstanding a classifier's predictionsA closer look at confusion matricesUsing confusion matrices to measure performanceBeyond accuracy – other measures of performanceThe kappa statisticSensitivity and specificityPrecision and recallThe F-measureVisualizing performance tradeoffs with ROC 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 in a simulated competition
Summary
12. Specialized Machine Learning Topics
Managing and preparing real-world dataMaking data "tidy" with the tidyverse packagesGeneralizing tabular data structures with tibbleSpeeding and simplifying data preparation with dplyrReading and writing to external data filesImporting tidy tables with readrImporting Microsoft Excel, SAS, SPSS, and Stata files with rioQuerying data in SQL databasesThe tidy approach to managing database connectionsUsing a database backend with dplyrA traditional approach to SQL connectivity with RODBC
Working with online data and services
Downloading the complete text of web pagesParsing the data within 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 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 multicore and snowTaking advantage of parallel with foreach and doParallelTraining and evaluating models in parallel with caretParallel cloud computing with MapReduce and HadoopParallel cloud computing with Apache SparkDeploying optimized learning algorithmsBuilding bigger regression models with biglmGrowing random forests faster with rangerGrowing massive random forests with bigrfA faster machine learning computing engine with H2OGPU computingFlexible numeric computing and machine learning with TensorFlowAn interface for deep learning with Keras
Summary
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Index

Content preview from Machine Learning with R - Third Edition

Summary

In this chapter, we learned about classification using Naive Bayes. This algorithm constructs tables of probabilities that are used to estimate the likelihood that new examples belong to various classes. The probabilities are calculated using a formula known as Bayes' theorem, which specifies how dependent events are related. Although Bayes' theorem can be computationally expensive, a simplified version that makes so-called "naive" assumptions about the independence of features is capable of handling much larger datasets.

The Naive Bayes classifier is often used for text classification. To illustrate its effectiveness, we employed Naive Bayes on a classification task involving spam SMS messages. Preparing the text data for analysis required ...

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Mastering Machine Learning with R - Third Edition

Publisher Resources

ISBN: 9781788295864

Cloud Computing

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Machine Learning with R - Third Edition

by Brett Lantz

Summary

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.