Index

A

• abstraction / Abstraction
• activation function / From biological to artificial neurons
  • about / Activation functions
  • threshold activation function / Activation functions
  • unit step activation function / Activation functions
  • sigmoid activation function / Activation functions
• AdaBoost
  • about / Boosting
• AdaBoost.M1 algorithm / Boosting
• adaptive boosting
  • about / Boosting the accuracy of decision trees, Boosting
• allocation function / Understanding ensembles
• Apache Hadoop
  • about / Parallel cloud computing with MapReduce and Hadoop
• Application Programming Interfaces (APIs)
  • about / Parsing JSON from web APIs
• Apriori
  • property / The Apriori algorithm for association rule learning
• Apriori algorithm
  • for association rule learning / The Apriori algorithm for association rule learning
  • strengths / The Apriori algorithm for association rule learning
• Apriori principle
  • used, for building set of rules / Building a set of rules with the Apriori principle
• Artificial Neural Network (ANN)
  • about / Understanding neural networks
• association rules
  • about / Understanding association rules
  • potential applications / Understanding association rules
  • rule interest, measuring / Measuring rule interest – support and confidence
  • set of rules, building with Apriori principle / Building a set of rules with the Apriori principle
  • frequently purchased groceries, identifying with / Example – identifying frequently purchased groceries with association rules
• automated parameter tuning
  • caret package used for / Using caret for automated parameter tuning
  • requisites / Using caret for automated parameter tuning
• axon
  • about / From biological to artificial neurons

B

• backpropagation
  • neural networks, training with / Training neural networks with backpropagation
  • about / Training neural networks with backpropagation
• bag-of-words / Step 2 – exploring and preparing the data
• bagging
  • about / Bagging
• bank loans example, with C5.0 decision trees
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • random training, creating / Data preparation – creating random training and test datasets
  • test datasets, creating / Data preparation – creating random training and test datasets
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• Bayesian methods
  • basic concepts / Basic concepts of Bayesian methods
• Bayesian methods,basics concepts
  • joint probability / Understanding joint probability
  • conditional probability / Computing conditional probability with Bayes' theorem
• Bayesian methods, basics concepts
  • probability / Understanding probability
• Beowulf cluster
  • about / Working in parallel with multicore and snow
• betweenness centrality
  • about / Analyzing and visualizing network data
• bias / The case of linearly separable data
• bias-variance tradeoff / Choosing an appropriate k
• biglm package
  • regression models, building / Building bigger regression models with biglm
• bigmemory package
  • massive matrices, using with / Using massive matrices with bigmemory
  • URL / Using massive matrices with bigmemory
• bigrf package
  • random forests, building / Growing bigger and faster random forests with bigrf
  • URL / Growing bigger and faster random forests with bigrf
• bimodal / Measuring the central tendency – the mode
• binning
  • about / Using numeric features with Naive Bayes
• bins
  • about / Using numeric features with Naive Bayes
• Bioconductor
  • about / Analyzing bioinformatics data
  • URL / Analyzing bioinformatics data
• bioinformatics
  • about / Analyzing bioinformatics data
• bioinformatics data
  • analyzing / Analyzing bioinformatics data
• bivariate relationships
  • about / Exploring relationships between variables
• blind tasting experience example / The k-NN algorithm
• blowby / Simple linear regression
• body mass index (BMI) / Step 1 – collecting data
• boosting
  • about / Boosting
• bootstrap aggregating
  • about / Bagging
• bootstrap sampling / Bootstrap sampling
• box-and-whiskers plot / Visualizing numeric variables – boxplots
• branches
  • about / Understanding decision trees
• breast cancer
  • diagnosing, with k-NN algorithm / Example – diagnosing breast cancer with the k-NN algorithm
• breast cancer example
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance

C

• C5.0 algorithm
  • about / The C5.0 decision tree algorithm
  • split, selecting / Choosing the best split
  • decision tree, pruning / Pruning the decision tree
• caret package
  • using, for automated parameter tuning / Using caret for automated parameter tuning
  • URL / Using caret for automated parameter tuning, Training and evaluating models in parallel with caret
  • used, for evaluating models in parallel / Training and evaluating models in parallel with caret
• categorical / Types of input data
• categorical variables
  • about / Exploring categorical variables
  • central tendency, measuring / Measuring the central tendency – the mode
• cell body / From biological to artificial neurons
• centroid / Using distance to assign and update clusters
• characteristics, neural networks
  • activation function / From biological to artificial neurons
  • network topology / From biological to artificial neurons
  • training algorithm / From biological to artificial neurons
• classification / Types of machine learning algorithms
• classification and regression training (caret package) / Beyond accuracy – other measures of performance
• Classification and Regression Tree (CART) algorithm / Understanding regression trees and model trees
• classification performance
  • measuring / Measuring performance for classification
• classification prediction data-classification prediction data
  • working with / Working with classification prediction data in R
• classification rules
  • about / Understanding classification rules
  • separate and conquer / Separate and conquer
  • 1 R algorithm / The 1R algorithm
  • RIPPER algorithm / The RIPPER algorithm
  • obtaining, from decision trees / Rules from decision trees
• class imbalance problem / Measuring performance for classification
• clustering / Types of machine learning algorithms
  • about / Understanding clustering
  • as machine learning task / Clustering as a machine learning task
• clustering, k-means clustering algorithm
  • about / The k-means clustering algorithm
  • distance, used for assigning cluster / Using distance to assign and update clusters
  • distance, used for updating cluster / Using distance to assign and update clusters
  • appropriate number of clusters, selecting / Choosing the appropriate number of clusters
• column-major order / Matrixes and arrays
• combination function / Understanding ensembles
• Complete Unified Device Architecture (CUDA)
  • about / GPU computing
• Comprehensive R Archive Network (CRAN)
  • about / Machine learning with R
  • URL / Machine learning with R
• concrete strength, modeling with ANNs
  • about / Example – Modeling the strength of concrete with ANNs
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• conditional probability
  • about / Computing conditional probability with Bayes' theorem
• confusion matrix
  • about / A closer look at confusion matrices
  • used, for measuring performance / Using confusion matrices to measure performance
• control object / Customizing the tuning process
• convex hull / The case of linearly separable data
• corpus / Data preparation – cleaning and standardizing text data
• correlation
  • about / Correlations
• CRAN
  • about / Improving the performance of R
  • URL / Improving the performance of R
• CRAN task view
  • URL / Analyzing bioinformatics data
• CRAN Web Technologies
  • URL / Working with online data and services
• cross-validation / Cross-validation
• CSV (Comma-Separated Values) file
  • about / Importing and saving data from CSV files
• CSV files
  • data, importing from / Importing and saving data from CSV files
• curl utility
  • about / Downloading the complete text of web pages
• cut points
  • about / Using numeric features with Naive Bayes

D

• data
  • managing, with R / Managing data with R
  • importing, from CSV files / Importing and saving data from CSV files
• data.table package
  • using / Making data frames faster with data.table
  • URL / Making data frames faster with data.table
• Database Management Systems (DBMSs)
  • about / Querying data in SQL databases
• databases
  • about / Working with proprietary files and databases
  • data, querying in SQL databases / Querying data in SQL databases
• data dictionary
  • about / Exploring the structure of data
• data exploration
  • about / Exploring and understanding data
• data frame
  • about / Data frames
• data mining
  • about / The origins of machine learning
• data munging
  • about / Working with proprietary files and databases
• data preparation, breast cancer example
  • training, creating / Data preparation – creating training and test datasets
  • test datasets, creating / Data preparation – creating training and test datasets
• Data Source Name (DSN)
  • about / Querying data in SQL databases
• data storage / Data storage
• data structures, R
  • about / R data structures
  • vector / Vectors
  • factor / Factors
  • lists / Lists
  • data frame / Data frames
  • matrix / Matrixes and arrays
  • array / Matrixes and arrays
  • saving / Saving, loading, and removing R data structures
  • loading / Saving, loading, and removing R data structures
  • removing / Saving, loading, and removing R data structures
  • exploring / Exploring the structure of data
• data table
  • about / Making data frames faster with data.table
• data wrangling
  • about / Working with proprietary files and databases
• decision nodes
  • about / Understanding decision trees
• decision tree
  • potential uses / Understanding decision trees
  • about / Understanding decision trees, Example – identifying risky bank loans using C5.0 decision trees
  • divide and conquer / Divide and conquer
  • pruning / Pruning the decision tree
  • used, for identifying risky bank loans / Example – identifying risky bank loans using C5.0 decision trees
  • accuracy, boosting / Boosting the accuracy of decision trees
• decision tree forests
  • about / Random forests
• decision trees
  • classification rules, obtaining from / Rules from decision trees
• deep learning
  • about / The direction of information travel
• Deep Neural Network (DNN)
  • about / The direction of information travel
• delimiter
  • about / Importing and saving data from CSV files
• dendrites
  • about / From biological to artificial neurons
• dependent events / Understanding joint probability
• dependent variable
  • about / Understanding regression
• descriptive model / Types of machine learning algorithms
• disk-based data frames
  • creating, with ff package / Creating disk-based data frames with ff
• divide and conquer
  • about / Divide and conquer
• domain-specific data
  • working with / Working with domain-specific data
  • bioinformatics data, analyzing / Analyzing bioinformatics data
  • network data, analyzing / Analyzing and visualizing network data
  • network data, visualizing / Analyzing and visualizing network data
• doParallel package
  • using / Taking advantage of parallel with foreach and doParallel
• dplyr package
  • used, for generalizing tabular data structures / Generalizing tabular data structures with dplyr
  • URL / Generalizing tabular data structures with dplyr
• dummy coding / Preparing data for use with k-NN, Step 3 – training a model on the data
• dummy variable / Examining relationships – two-way cross-tabulations, Step 3 – training a model on the data

E

• early stopping
  • about / Pruning the decision tree
• edgelist
  • about / Analyzing and visualizing network data
• elements
  • about / Vectors
• embarrassingly parallel problems
  • about / Learning faster with parallel computing
• ensemble methods
  • bagging / Bagging
  • boosting / Boosting
  • random forests / Random forests
• ensembles
  • about / Understanding ensembles
  • advantages / Understanding ensembles
• entropy
  • about / Choosing the best split
• epoch
  • about / Training neural networks with backpropagation
  • forward phase / Training neural networks with backpropagation
  • backward phase / Training neural networks with backpropagation
• erosion / Simple linear regression
• Euclidean norm / The case of linearly separable data
• evaluation / Evaluation

F

• 10-fold cross-validation (10-fold CV) / Cross-validation
• F-measure / The F-measure
• F-score / The F-measure
• F1 score / The F-measure
• factor
  • about / Factors
• feedforward networks
  • about / The direction of information travel
• ffbase project
  • URL / Creating disk-based data frames with ff
• ff package
  • used, for creating disk-based data frames / Creating disk-based data frames with ff
  • URL / Creating disk-based data frames with ff
• five-number summary / Measuring spread – quartiles and the five-number summary
• foreach package
  • using / Taking advantage of parallel with foreach and doParallel
• frequently purchased groceries
  • identifying, with association rules / Example – identifying frequently purchased groceries with association rules
• future performance
  • estimating / Estimating future performance
• future performance estimation
  • holdout method / The holdout method
  • cross-validation / Cross-validation
  • bootstrap sampling / Bootstrap sampling

G

• Gaussian RBF kernel / Using kernels for non-linear spaces
• generalization / Generalization
• Generalized Linear Models (GLM) / Understanding regression
• glyph / Step 1 – collecting data
• GPU
  • about / GPU computing
  • computing / GPU computing
  • URL / GPU computing
• gradient descent / Training neural networks with backpropagation
• Graph Modeling Language (GML)
  • about / Analyzing and visualizing network data
• greedy learners / What makes trees and rules greedy?
• grid
  • about / Learning faster with parallel computing

H

• Hadoop
  • using / Parallel cloud computing with MapReduce and Hadoop
  • URL / Parallel cloud computing with MapReduce and Hadoop
• harmonic mean / The F-measure
• header line
  • about / Importing and saving data from CSV files
• histograms / Visualizing numeric variables – histograms
• holdout method / The holdout method, Cross-validation
• httr package
  • URL / Downloading the complete text of web pages
• hyperplane / Understanding Support Vector Machines
• Hypertext Markup Language (HTML)
  • about / Downloading the complete text of web pages

I

• igraph package
  • about / Analyzing and visualizing network data
  • URL / Analyzing and visualizing network data
• imputation / Data preparation – imputing the missing values
• Incremental Reduced Error Pruning (IREP) algorithm / The RIPPER algorithm
• independent events / Understanding joint probability
• independent variables
  • about / Understanding regression
• information gain / Choosing the best split
• input data
  • types / Types of input data
  • matching, to algorithms / Matching input data to algorithms
• input nodes / The number of layers
• instance-based learning
  • about / Why is the k-NN algorithm lazy?
• intercept
  • about / Understanding regression
• Interquartile Range (IQR) / Measuring spread – quartiles and the five-number summary
• itemset
  • about / Understanding association rules
• Iterative Dichotomiser 3 (ID3) / The C5.0 decision tree algorithm

J

• joint probability / Understanding joint probability
• JSON
  • parsing, from web APIs / Parsing JSON from web APIs
  • about / Parsing JSON from web APIs
  • URL / Parsing JSON from web APIs
• jsonlite package
  • URL / Parsing JSON from web APIs

K

• k-fold cross-validation (or k-fold CV) / Cross-validation
• k-means++ / Using distance to assign and update clusters
• k-means clustering algorithm
  • about / The k-means clustering algorithm
• k-NN algorithm
  • about / The k-NN algorithm
  • weaknesses / The k-NN algorithm
  • similarity, measuring with distance / Measuring similarity with distance
  • appropriate k, selecting / Choosing an appropriate k
  • data, preparing / Preparing data for use with k-NN
  • lazy learning algorithm / Why is the k-NN algorithm lazy?
  • used, for diagnosing breast cancer / Example – diagnosing breast cancer with the k-NN algorithm
• kernels
  • using, for non-linear spaces / Using kernels for non-linear spaces
• kernel trick / Using kernels for non-linear spaces
• kernlab
  • reference / Step 3 – training a model on the data

L

• Laplace estimator
  • about / The Laplace estimator
• large datasets
  • managing / Managing very large datasets
  • tabular data structures, generalizing with dplyr / Generalizing tabular data structures with dplyr
  • data.table package, using / Making data frames faster with data.table
  • disk-based data frames, creating with ff package / Creating disk-based data frames with ff
  • massive matrices, using with bigmemory package / Using massive matrices with bigmemory
• latitude / Using kernels for non-linear spaces
• layers
  • about / The number of layers
• lazy learning algorithms / Why is the k-NN algorithm lazy?
• leaf nodes
  • about / Understanding decision trees
• learning rate / Training neural networks with backpropagation
• leave-one-out method / Cross-validation
• left-hand side (LHS) / Understanding association rules
• levels / Types of machine learning algorithms
• LIBSVM
  • URL / Step 3 – training a model on the data
• likelihood
  • about / Computing conditional probability with Bayes' theorem
• linear kernel / Using kernels for non-linear spaces
• link function / Understanding regression
• lists / Lists
• loess curve / Visualizing relationships among features – the scatterplot matrix
• logistic regression
  • about / Understanding regression
• longitude / Using kernels for non-linear spaces

M

• machine learning
  • origins / The origins of machine learning
  • about / The origins of machine learning
  • abuses / Uses and abuses of machine learning
  • uses / Uses and abuses of machine learning
  • successes / Machine learning successes
  • limitations / The limits of machine learning
  • ethics / Machine learning ethics
  • process / How machines learn
  • with R / Machine learning with R
  • R packages, installing / Installing R packages
  • R packages, loading / Loading and unloading R packages
  • R packages, unloading / Loading and unloading R packages
• machine learning, in practice
  • about / Machine learning in practice
  • data collection / Machine learning in practice
  • data exploration and preparation / Machine learning in practice
  • model training / Machine learning in practice
  • model evaluation / Machine learning in practice
  • model improvement / Machine learning in practice
  • input data, types / Types of input data
  • algorithms, types / Types of machine learning algorithms
  • input data, matching to algorithms / Matching input data to algorithms
• machine learning, process
  • about / How machines learn
  • data storage / How machines learn, Data storage
  • abstraction / How machines learn, Abstraction
  • generalization / How machines learn, Generalization
  • evaluation / How machines learn, Evaluation
• machine learning algorithms
  • types / Types of machine learning algorithms
• magrittr package
  • about / Scraping data from web pages
  • URL / Scraping data from web pages
• MapReduce
  • about / Parallel cloud computing with MapReduce and Hadoop
  • map step / Parallel cloud computing with MapReduce and Hadoop
  • reduce step / Parallel cloud computing with MapReduce and Hadoop
• marginal likelihood
  • about / Computing conditional probability with Bayes' theorem
• market basket analysis example
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • sparse matrix, creating for transaction data / Data preparation – creating a sparse matrix for transaction data
  • item support, visualizing / Visualizing item support – item frequency plots
  • transaction data, visualizing / Visualizing the transaction data – plotting the sparse matrix
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
  • set of association rules, sorting / Sorting the set of association rules
  • subset of association rules, sorting / Taking subsets of association rules
  • association rules, saving to file / Saving association rules to a file or data frame
  • association rules, saving to data frame / Saving association rules to a file or data frame
• matrix
  • about / Matrixes and arrays
  / Matrixes and arrays
• matrix notation / Multiple linear regression
• maximum margin hyperplane (MMH) / Classification with hyperplanes
• mean / Measuring the central tendency – mean and median
• mean absolute error (MAE) / Measuring performance with the mean absolute error
• medical expenses, predicting with linear regression
  • about / Example – predicting medical expenses using linear regression
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • correlation matrix / Exploring relationships among features – the correlation matrix
  • relationships, visualizing among features / Visualizing relationships among features – the scatterplot matrix
  • scatterplot matrix / Visualizing relationships among features – the scatterplot matrix
  • model, training on data / Step 3 – training a model on the data
  • model performance, training / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance, Model specification – adding non-linear relationships, Transformation – converting a numeric variable to a binary indicator, Model specification – adding interaction effects, Putting it all together – an improved regression model
• message-passing interface (MPI)
  • about / Working in parallel with multicore and snow
• meta-learners / Types of machine learning algorithms
• meta-learning methods
  • used, for improving model performance / Improving model performance with meta-learning
  • about / Improving model performance with meta-learning
• min-max normalization / Preparing data for use with k-NN
• mobile phone spam
  • filtering, with Naive Bayes algorithm / Example – filtering mobile phone spam with the Naive Bayes algorithm
• mobile phone spam example
  • data, collecting / Step 1 – collecting data
  • dat a collecting, URL / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • text data, cleaning / Data preparation – cleaning and standardizing text data
  • text data, standardizing / Data preparation – cleaning and standardizing text data
  • text documents, splitting into words / Data preparation – splitting text documents into words
  • training, creating / Data preparation – creating training and test datasets
  • test datasets, creating / Data preparation – creating training and test datasets
  • text data, visualizing / Visualizing text data – word clouds
  • indicator features, creating for frequent words / Data preparation – creating indicator features for frequent words
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• model performance
  • improving, with meta-learning / Improving model performance with meta-learning
• model performance, breast cancer example
  • z-score standardization / Transformation – z-score standardization
  • alternatives values, testing of k / Testing alternative values of k
• model trees / Understanding regression trees and model trees
• multicore package
  • using / Working in parallel with multicore and snow
• multilayer network
  • about / The number of layers
• Multilayer Perceptron (MLP)
  • about / The direction of information travel
• multimodal / Measuring the central tendency – the mode
• multinomial logistic regression / Understanding regression
• multiple linear regression / Understanding regression
  • about / Multiple linear regression
  • weaknesses / Multiple linear regression
• multiple R-squared value (coefficient of determination) / Step 4 – evaluating model performance
• multivariate relationships
  • about / Exploring relationships between variables

N

• Naive Bayes algorithm
  • about / Understanding Naive Bayes, The Naive Bayes algorithm
  • classification / Classification with Naive Bayes
  • Laplace estimator / The Laplace estimator
  • numeric features, using with / Using numeric features with Naive Bayes
  • used, for filtering mobile phone spam / Example – filtering mobile phone spam with the Naive Bayes algorithm
• nearest neighbor classification
  • about / Understanding nearest neighbor classification
• network analysis
  • about / Analyzing and visualizing network data
• network data
  • analyzing / Analyzing and visualizing network data
  • visualizing / Analyzing and visualizing network data
• network topology
  • about / Network topology
  • layers / The number of layers
  • direction of information travel / The direction of information travel
  • number of nodes in each layer / The number of nodes in each layer
• neural networks
  • about / Understanding neural networks
  • biological, to artificial neurons / From biological to artificial neurons
  • characteristics / From biological to artificial neurons
  • training, with backpropagation / Training neural networks with backpropagation
• neurons
  • about / Understanding neural networks
• nodes / Understanding neural networks
• nominal / Types of input data
• nominal variables
  • about / Factors
• non-linear spaces
  • kernels, using for / Using kernels for non-linear spaces
• normal distribution / Understanding numeric data – uniform and normal distributions
• numeric / Types of input data
• numeric data
  • about / Understanding numeric data – uniform and normal distributions
  • normalizing / Transformation – normalizing numeric data
• numeric features
  • using, with Naive Bayes / Using numeric features with Naive Bayes
• numeric prediction / Types of machine learning algorithms
• numeric variables
  • about / Exploring numeric variables
  • central tendency, measuring / Measuring the central tendency – mean and median
  • spread, measuring / Measuring spread – quartiles and the five-number summary, Measuring spread – variance and standard deviation
  • visualizing / Visualizing numeric variables – boxplots, Visualizing numeric variables – histograms

O

• OCR, performing with SVMs
  • about / Example – performing OCR with SVMs
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• one-way table / Exploring categorical variables
• online data
  • working with / Working with online data and services
  • parsing / Working with online data and services
  • complete text of web pages, downloading / Downloading the complete text of web pages
  • parsing, within web pages / Scraping data from web pages
• online services
  • working with / Working with online data and services
• Open Database Connectivity (ODBC)
  • about / Querying data in SQL databases
• optimized learning algorithms
  • deploying / Deploying optimized learning algorithms
  • regression models, building with biglm package / Building bigger regression models with biglm
  • random forests, building with bigrf package / Growing bigger and faster random forests with bigrf
  • models in parallel, evaluating with caret package / Training and evaluating models in parallel with caret
• ordinal / Types of input data
• ordinary least squares estimation
  • about / Ordinary least squares estimation
• out-of-bag error rate / Training random forests
• overfitting / Evaluation

P

• parallel cloud computing
  • with MapReduce / Parallel cloud computing with MapReduce and Hadoop
  • with Hadoop / Parallel cloud computing with MapReduce and Hadoop
• parallel computing
  • about / Learning faster with parallel computing
  • execution time, measuring / Measuring execution time
  • with multicore package / Working in parallel with multicore and snow
  • with snow package / Working in parallel with multicore and snow
  • with foreach package / Taking advantage of parallel with foreach and doParallel
  • with doParallel package / Taking advantage of parallel with foreach and doParallel
• parameter tuning
  • about / Tuning stock models for better performance
• pattern discovery / Types of machine learning algorithms
• Pearson's correlation coefficient / Correlations
• performance
  • measuring, confusion matrices used / Using confusion matrices to measure performance
• performance measures
  • about / Beyond accuracy – other measures of performance
  • kappa statistic / The kappa statistic
  • sensitivity / Sensitivity and specificity
  • specificity / Sensitivity and specificity
  • precision / Precision and recall
• performance tradeoffs
  • -visualizing / Visualizing performance trade-offs
• poisonous mushrooms
  • identifying, with rule learners / Example – identifying poisonous mushrooms with rule learners
• poisonous mushrooms example, with rule learners
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• Poisson regression
  • about / Understanding regression
• polynomial kernel / Using kernels for non-linear spaces
• positive predictive value / Precision and recall
• posterior probability
  • about / Computing conditional probability with Bayes' theorem
• postpruning
  • about / Pruning the decision tree
• pre-pruning
  • about / Pruning the decision tree
• precision / Precision and recall
• predictive model / Types of machine learning algorithms
• prior probability
  • about / Computing conditional probability with Bayes' theorem
• probability
  • about / Understanding probability
• proprietary files
  • about / Working with proprietary files and databases
  • Microsoft Excel files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • Microsoft Excel files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SAS files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SAS files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SPSS files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • SPSS files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • Stata files, writing / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • Stata files, reading / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
• proprietary microarray
  • using / Analyzing bioinformatics data
• pure / Choosing the best split
• purity / Choosing the best split

Q

• quadratic optimization / The case of linearly separable data
• quantiles / Measuring spread – quartiles and the five-number summary

R

• 1 R algorithm / The 1R algorithm
• R
  • about / Machine learning with R
  • packages, installing / Installing R packages
  • packages, loading / Loading and unloading R packages
  • packages, unloading / Loading and unloading R packages
  • data structures / R data structures
  • used, for managing data / Managing data with R
  • working with classification prediction data / Working with classification prediction data in R
• R, performance improvement
  • about / Improving the performance of R
  • large datasets, managing / Managing very large datasets
  • parallel computing / Learning faster with parallel computing
  • GPU, computing / GPU computing
  • optimized learning algorithms, deploying / Deploying optimized learning algorithms
• R-squared value / Step 4 – evaluating model performance
• Radial Basis Function (RBF) network
  • about / Activation functions
• random forests
  • about / Random forests
  • URL / Random forests
  • strengths / Random forests
  • training / Training random forests
  • performance, evaluating / Evaluating random forest performance
  • building, with bigrf package / Growing bigger and faster random forests with bigrf
• RCurl
  • URL / Downloading the complete text of web pages
• rea under the ROC curve (AUC) / ROC curves
• Receiver Operating Characteristic (ROC) curve
  • about / ROC curves
  • creating / ROC curves
• recurrent network
  • about / The direction of information travel
• recursive partitioning
  • about / Divide and conquer
• regression
  • about / Understanding regression
  • simple linear regression / Simple linear regression
  • ordinary least squares estimation / Ordinary least squares estimation
  • correlation / Correlations
  • multiple linear regression / Multiple linear regression
  • adding, to trees / Adding regression to trees
• regression analysis
  • use cases / Understanding regression
• regression equations
  • about / Understanding regression
• regression models
  • building, with biglm package / Building bigger regression models with biglm
• regression trees
  • about / Understanding regression trees and model trees
• relationships
  • exploring, between variables / Exploring relationships between variables
  • visualizing / Visualizing relationships – scatterplots
  • examining / Examining relationships – two-way cross-tabulations
• Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm / The RIPPER algorithm
• residuals / Ordinary least squares estimation
• resubstitution error / Estimating future performance
• Revolution Analytics
  • URL / Taking advantage of parallel with foreach and doParallel
• RHadoop
  • URL / Parallel cloud computing with MapReduce and Hadoop
• RHIPE package
  • URL / Parallel cloud computing with MapReduce and Hadoop
• rio package
  • URL / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
  • about / Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files
• RIPPER algorithm
  • about / The RIPPER algorithm
• risky bank loans
  • identifying, C5.0 decision trees used / Example – identifying risky bank loans using C5.0 decision trees
• rote learning
  • about / Why is the k-NN algorithm lazy?
• rpart.plot
  • URL / Visualizing decision trees
• rudimentary ANNs / Understanding neural networks
• rvest package
  • about / Scraping data from web pages

S

• scatterplot
  • about / Visualizing relationships – scatterplots
• scatterplot matrix (SPLOM) / Visualizing relationships among features – the scatterplot matrix
• Scoville scale / Preparing data for use with k-NN
• segmentation analysis / Types of machine learning algorithms
• semi-supervised learning / Clustering as a machine learning task
• separate and conquer
  • about / Separate and conquer
• sigmoid kernel / Using kernels for non-linear spaces
• simple linear regression / Understanding regression
  • about / Simple linear regression
• simple tuned model
  • creating / Creating a simple tuned model
• slack variable / The case of nonlinearly separable data
• slope
  • about / Understanding regression
• slope-intercept form
  • about / Understanding regression
• SMS Spam Collection
  • URL / Step 1 – collecting data
• snowball
  • URL / Data preparation – cleaning and standardizing text data
• snow package
  • using / Working in parallel with multicore and snow
  • URL / Working in parallel with multicore and snow
• social networking service (SNS) / Example – finding teen market segments using k-means clustering
• sparse matrix / Data preparation – splitting text documents into words, Data preparation – creating a sparse matrix for transaction data
• SQL databases
  • data, querying in / Querying data in SQL databases
• squashing functions / Activation functions
• stacking
  • about / Understanding ensembles
• standard deviation
  • about / Measuring spread – variance and standard deviation
• standard deviation reduction (SDR) / Adding regression to trees
• statistical hypothesis testing / Understanding regression
• stock models
  • tuning, for better performance / Tuning stock models for better performance
• Structured Query Language (SQL)
  • about / Querying data in SQL databases
• subtree raising / Pruning the decision tree
• subtree replacement / Pruning the decision tree
• summary statistics / Exploring numeric variables
• supervised learning / Types of machine learning algorithms
• Support Vector Machine (SVM)
  • about / Understanding Support Vector Machines
  • applications / Understanding Support Vector Machines
  • classifications, with hyperplanes / Classification with hyperplanes
  • case of linearly separable data / The case of linearly separable data
  • case of nonlinearly separable data / The case of nonlinearly separable data
  • OCR, performing with / Example – performing OCR with SVMs
  / Bagging
• support vectors / Classification with hyperplanes
• SVMlight
  • about / Step 3 – training a model on the data
  • URL / Step 3 – training a model on the data
• synapse
  • about / From biological to artificial neurons

T

• Tab-Separated Value (TSV)
  • about / Importing and saving data from CSV files
• tabular
  • about / Importing and saving data from CSV files
• tabular data structures
  • generalizing, with dplyr package / Generalizing tabular data structures with dplyr
• teen market segments search, with k-means clustering
  • about / Example – finding teen market segments using k-means clustering
  • data, collecting / Step 1 – collecting data
  • data, exploring / Step 2 – exploring and preparing the data
  • data, preparing / Step 2 – exploring and preparing the data, Data preparation – dummy coding missing values, Data preparation – imputing the missing values
  • model, training on data / Step 3 – training a model on the data
  • model performance, evaluating / Step 4 – evaluating model performance
  • model performance, improving / Step 5 – improving model performance
• terminal nodes / Understanding decision trees
• threshold activation function / Activation functions
• training / Abstraction
• trees
  • regression, adding to / Adding regression to trees
• tree structure
  • about / Understanding decision trees
• tuning process
  • customizing / Customizing the tuning process
• two-way cross-tabulation
  • about / Examining relationships – two-way cross-tabulations

U

• UCI Machine Learning Data Repository
  • URL / Step 1 – collecting data, Step 1 – collecting data, Step 1 – collecting data
  • about / Step 1 – collecting data
• unimodal / Measuring the central tendency – the mode
• unit of analysis / Types of input data
• unit of observation / Types of input data
• unit step activation function / Activation functions
• univariate statistics
  • about / Exploring relationships between variables
• universal function approximator / The number of nodes in each layer
• unsupervised learning / Types of machine learning algorithms

V

• vector
  • about / Vectors
• vector types
  • types / Vectors
• Voronoi diagram / Using distance to assign and update clusters

W

• web pages
  • complete text, downloading / Downloading the complete text of web pages
  • data, parsing / Scraping data from web pages
  • XML documents, parsing / Parsing XML documents
  • JSON, parsing from web APIs / Parsing JSON from web APIs
• web scraping
  • about / Scraping data from web pages
• wine quality estimation, with regression trees
  • about / Example – estimating the quality of wines with regression trees and model trees
  • data, collecting / Step 1 – collecting data
  • data, preparing / Step 2 – exploring and preparing the data
  • data, exploring / Step 2 – exploring and preparing the data
  • model, training on data / Step 3 – training a model on the data
  • decision trees, visualizing / Visualizing decision trees
  • model performance, evaluating / Step 4 – evaluating model performance
  • performance, measuring with mean absolute error / Measuring performance with the mean absolute error
  • model performance, improving / Step 5 – improving model performance
• word cloud
  • about / Visualizing text data – word clouds
• wordcloud package
  • URL / Visualizing text data – word clouds

X

• xml2 GitHub
  • URL / Parsing XML documents
• XML documents
  • parsing / Parsing XML documents
• XML package
  • about / Parsing XML documents
  • URL / Parsing XML documents

Z

• z-score / Preparing data for use with k-NN
• z-score standardization / Preparing data for use with k-NN, Transformation – z-score standardization
• ZeroR / The 1R algorithm