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
• active-user / Result interpretation
• AdaBoost
  • about / Boosting
• AdaBoost.M1 algorithm / Boosting
• adaptive boosting
  • about / Boosting the accuracy of decision trees, Boosting
• Adaptive Boosting (AdaBoost) / Boosting
• Aikake's Information Criterion (AIC) / Modeling and evaluation
  • about / Granger causality
• algorithm flowchart
  • about / Algorithm flowchart
• algorithms
  • family / Families of algorithms
  • supervised learning algorithms / Supervised learning algorithms
  • linear regression / Linear regression
  • K-Nearest Neighbors (KNN) / K-Nearest Neighbors (KNN)
• allocation function / Understanding ensembles
• American Diabetes Association (ADA)
  • URL / Business understanding
• analytics
  • about / Types of analytics
  • descriptive / Types of analytics
  • diagnostic / Types of analytics
  • predictive / Types of analytics
  • prescriptive / Types of analytics
• Apache Hadoop
  • about / Parallel cloud computing with MapReduce and Hadoop
• Application Programming Interfaces (APIs)
  • about / Parsing JSON from web APIs
• apply function / apply
• Apriori
  • property / The Apriori algorithm for association rule learning
• Apriori algorithm / Apriori algorithm
  • for association rule learning / The Apriori algorithm for association rule learning
  • strengths / The Apriori algorithm for association rule learning
• apriori algorithms
  • about / An overview of a market basket analysis
• Apriori principle
  • used, for building set of rules / Building a set of rules with the Apriori principle
• Area under curve (AUC) / Evaluating predictive models
• Area Under the Curve (AUC)
  • about / Model selection
• arrays and matrices
  • about / Arrays and matrices
  • creating / Creating arrays and matrices
• Artificial Neural Network (ANN)
  • about / Understanding neural networks
• Artificial Neural Networks (ANNs)
  • about / Neural network
  • reference link / Neural network
• arules* Mining Association Rules and Frequent Itemsets
  • about / An overview of a market basket analysis
• Association for Computational Linguistics (ACL) / Feature extraction
• association rule mining
  • about / Association rule mining
  • dependencies and data, loading / Loading dependencies and data
  • exploratory analysis / Exploratory analysis
  • shopping trends, detecting / Detecting and predicting shopping trends
  • shopping trends, predicting / Detecting and predicting shopping trends
  • association rules, visualizing / Visualizing association rules
• 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
• Augmented Dickey-Fuller (ADF) test
  • about / Data understanding and preparation
• Autocorrelation Function (ACF)
  • about / Univariate time series analysis
• automated parameter tuning
  • caret package used for / Using caret for automated parameter tuning
  • requisites / Using caret for automated parameter tuning
• Autoregressive Integrated Moving Average (ARIMA) models
  • about / Univariate time series analysis
• axon
  • about / From biological to artificial neurons

B

• Back Propagation
  • about / Neural network
• backpropagation
  • neural networks, training with / Training neural networks with backpropagation
  • about / Training neural networks with backpropagation
• backward stepwise regression / Modeling and evaluation
• bag-of-words / Step 2 – exploring and preparing the data
• bagging
  • about / Bagging, Random forest
• 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 Information Criterion (BIC) / Modeling and evaluation
• 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 trade-off
  • about / Discriminant analysis overview
• 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 regression
  • for univariate time series / Bivariate regression
• bivariate relationships
  • about / Exploring relationships between variables
• blind tasting experience example / The k-NN algorithm
• blogs / References
• blowby / Simple linear regression
• body mass index (BMI) / Step 1 – collecting data
• boosting / Boosting
  • about / Boosting
• bootstrap aggregating
  • about / Bagging
• bootstrap aggregation
  • about / Random forest
• 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
• Breusch-Pagan (BP) / Modeling and evaluation
• business case, regularization
  • about / Business case
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
• business understanding, CRISP-DM process
  • about / Business understanding
  • business objective, identifying / Identify the business objective
  • situation, assessing / Assess the situation
  • analytical goals, determining / Determine the analytical goals
  • project plan, producing / Produce a project plan

C

• C5.0 algorithm
  • about / The C5.0 decision tree algorithm
  • split, selecting / Choosing the best split
  • decision tree, pruning / Pruning the decision tree
• Carbon Dioxide Information Analysis Center (CDIAC)
  • URL / Business understanding
• 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, Elastic net
  • used, for evaluating models in parallel / Training and evaluating models in parallel with caret
  • about / Elastic net
• CART / Modeling using decision trees
• 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 methods
  • about / Classification methods and linear regression
• classification models
  • selecting / Model selection
• 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
• classification trees
  • overview / Classification trees
  • business case / Business case
  • evaluation / Classification tree
  • modeling / Classification tree
• class imbalance problem / Measuring performance for classification
• cluster analysis
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • with mixed data / Clustering with mixed data
• 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
• code flow
  • controlling / Controlling code flow
  • if / Working with if, if-else, and ifelse
  • if-else / Working with if, if-else, and ifelse
  • ifelse / Working with if, if-else, and ifelse
  • switch / Working with switch
  • loops / Loops
• Cohen's Kappa statistic
  • about / KNN modeling
• collaborative filtering
  • about / An overview of a recommendation engine
  • user-based collaborative filtering (UBCF) / User-based collaborative filtering
  • item-based collaborative filtering (IBCF) / Item-based collaborative filtering
  • singular value decomposition (SVD) / Singular value decomposition and principal components analysis
  • principal components analysis (PCA) / Singular value decomposition and principal components analysis
• collaborative filters
  • about / Collaborative filters
  • core concepts / Core concepts and definitions
  • user / Core concepts and definitions
  • item / Core concepts and definitions
  • rating / Core concepts and definitions
  • ratings matrix / Core concepts and definitions
  • sparse matrix / Core concepts and definitions
  • algorithm / The collaborative filtering algorithm
  • predictions / Predictions
  • recommendations / Recommendations
  • similarity measure / Similarity
• column-major order / Matrixes and arrays
• combination function / Understanding ensembles
• Complete Unified Device Architecture (CUDA)
  • about / GPU computing
• Comprehensive R Archive Network (CRAN) / Advanced contingency matrices
  • 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
• constructs
  • about / Advanced constructs
  • lapply and sapply / lapply and sapply
  • apply function / apply
  • tapply function / tapply
  • mapply function / mapply
• content-based recommender engines / Understanding recommendation systems
• control object / Customizing the tuning process
• convex hull / The case of linearly separable data
• Cook's distance / Business understanding
• Corpus
  • about / Text mining framework and methods
• corpus / Data preparation – cleaning and standardizing text data
• correlation
  • about / Correlations
• Cosine Similarity
  • about / User-based collaborative filtering
• cosine similarity measure / Similarity
• 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
• credit risk
  • about / What is credit risk?
  • predicting / How to predict credit risk
• CRISP-DM process
  • about / The process
  • URL / The process
  • business understanding / Business understanding
  • data, understanding / Data understanding
  • data preparation / Data preparation
  • modeling / Modeling
  • evaluation / Evaluation
  • deployment / Deployment
  • algorithm flowchart / Algorithm flowchart
• Cross-Entropy
  • about / Neural network
• cross-validation / Cross-validation, Cross-validation
  • for logistic regression / Logistic regression with cross-validation
• Cross Correlation Function (CCF)
  • about / Data understanding and preparation
• crossTable / Techniques used for analysis
• CRUTEM4 surface air temperature
  • URL / Business understanding
• 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

• D3
  • URL / References
• data
  • getting / Getting the data, Predictive analytics, Getting the data
  • preprocessing / Predictive analytics
  • reprocessing / Data preprocessing
  • about / Data and visualization
  • 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
• data analysis
  • and transformation / Data analysis and transformation
  • utilities, building / Building analysis utilities
  • dataset, analyzing / Analyzing the dataset
  • transformed dataset, saving / Saving the transformed dataset
• 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
  • creating / Data frames and matrices
• data frames
  • about / Data frames
  • creating / Creating data frames
  • operating on / Operating on data frames
• data mining
  • about / Data mining @social networks, The origins of machine learning
  • social network data, mining / Mining social network data
• 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 preparation process
  • about / Data preparation
• data preprocessing
  • about / Data preprocessing
  • missing values, dealing with / Dealing with missing values
  • datatype conversions / Datatype conversions
  • numeric variables / Datatype conversions
  • categorical variables / Datatype conversions
• dataset
  • URL / Getting the data
  • preparing / Predictive analytics
• 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 understanding process
  • about / Data understanding
• 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
  • used, for modeling / Modeling using decision trees
  • classification rules, obtaining from / Rules from decision trees
• deep learning
  • about / The direction of information travel
  • overview / Deep learning, a not-so-deep overview
  • reference link / Deep learning, a not-so-deep overview, Modeling
  • example / An example of deep learning
  • H2O / H2O background
• 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
• dendrogram / Hierarchical clustering
• dependent events / Understanding joint probability
• dependent variable
  • about / Understanding regression
• deployment process / Deployment
• descriptive analytics / Types of analytics, Our next challenge
• descriptive model / Types of machine learning algorithms
• diagnostic analytics / Types of analytics
• dirichlet distribution
  • about / Topic models
• Discriminant Analysis (DA)
  • overview / Discriminant analysis overview
  • Linear Discriminant Analysis (LDA) / Discriminant analysis overview
  • Quadratic Discriminant Analysis (QDA) / Discriminant analysis overview
  • application / Discriminant analysis application
• disk-based data frames
  • creating, with ff package / Creating disk-based data frames with ff
• divide and conquer
  • about / Divide and conquer
• Document-Term Matrix (DTM)
  • about / Text mining framework and methods
• 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
• dynamic topic modelling
  • about / Topic models

E

• early stopping
  • about / Pruning the decision tree
• ECLAT algorithms
  • about / An overview of a market basket analysis
• edgelist
  • about / Analyzing and visualizing network data
• eigenvalues
  • about / An overview of the principal components
• eigenvectors
  • about / An overview of the principal components
• elastic net
  • about / Elastic net
  • using / Elastic net
• elements
  • about / Vectors
• embarrassingly parallel problems
  • about / Learning faster with parallel computing
• Empirical Methods in Natural Language Processing (EMNLP) / Feature extraction
• ensemble methods / Ensemble methods
  • bagging / Bagging
  • boosting / Boosting
  • random forests / Random forests
• ensembles
  • about / Understanding ensembles
  • advantages / Understanding ensembles
• entropy
  • about / Choosing the best split
• epoch / Implementation
  • about / Training neural networks with backpropagation
  • forward phase / Training neural networks with backpropagation
  • backward phase / Training neural networks with backpropagation
• equimax
  • about / Rotation
• erosion / Simple linear regression
• error / Matrix factorization
• Euclidean norm / The case of linearly separable data
• Euclidean norms / Similarity
• Euclidian Distance
  • about / K-Nearest Neighbors
• evaluation / Evaluation
• evaluation process
  • about / Evaluation
• exponential smoothing models
  • about / Univariate time series analysis
• Extract, Transport, and Load (ETL)
  • about / Data understanding

F

• 10-fold cross-validation (10-fold CV) / Cross-validation
• F-Measure
  • about / Other quantitative analyses
• F-measure / The F-measure
• F-score / The F-measure
• F1 score / The F-measure
• factor
  • about / Factors
• fall-out / Evaluating predictive models
• False Negative (FN) / Evaluating predictive models
• false negative rate / Evaluating predictive models
• False Positive (FP) / Evaluating predictive models
• false positive rate / Evaluating predictive models
• False Positive Rate (FPR)
  • about / Model selection
• feature
  • selecting / Predictive analytics, Feature selection
• feature sets
  • about / Next steps, Feature sets
• Feed Forward network
  • about / Neural network
• 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
• Final Prediction Error (FPE)
  • about / Granger causality
• Fine Needle Aspiration (FNA)
  • about / Business understanding
• first principal component
  • about / An overview of the principal components
• five-number summary / Measuring spread – quartiles and the five-number summary
• foreach package
  • using / Taking advantage of parallel with foreach and doParallel
• forward stepwise selection / Modeling and evaluation
• frequent itemset generation technique
  • about / Frequent itemset generation, Getting started
  • data retrieval and transformation / Data retrieval and transformation
  • itemset association matrix, building / Building an itemset association matrix
  • workflow, creating / Creating a frequent itemsets generation workflow
  • shopping trends, detecting / Detecting shopping trends
• Frequent Itemsets / Apriori algorithm
• frequently purchased groceries
  • identifying, with association rules / Example – identifying frequently purchased groceries with association rules
• functions
  • working with / Working with functions
  • built-in functions / Built-in functions
  • user-defined functions / User-defined functions
  • passing, as arguments / Passing functions as arguments
• 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
• Gedeon Method
  • about / Modeling
• generalization / Generalization
• Generalized Linear Models (GLM) / Understanding regression
• ggvis
  • URL / Collecting and exploring data
• glmnet package
  • used, for performing cross-validation for regularization / Cross-validation with glmnet
• glyph / Step 1 – collecting data
• Gower
  • about / Gower and partitioning around medoids
• GPU
  • about / GPU computing
  • computing / GPU computing
  • URL / GPU computing
• gradient boosted trees
  • about / Introduction
• gradient boosting
  • overview / Gradient boosting
  • reference link / Gradient boosting
  • business case / Business case
  • model selection / Model selection
• gradient boosting classification
  • modeling / Gradient boosting classification
  • evaluation / Gradient boosting classification
• gradient boosting regression
  • evaluation / Gradient boosting regression
  • modeling / Gradient boosting regression
• gradient descent / Matrix factorization, Training neural networks with backpropagation
• Granger causality
  • about / Granger causality
• Graphical User Interface (GUI) / Delving into the basics of R
  • about / Getting R up and running
• 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

• H2O
  • about / H2O background
  • URL / H2O background
  • data, preparing / Data preparation and uploading it to H2O
  • data, uploading / Data preparation and uploading it to H2O
  • train dataset, creating / Create train and test datasets
  • test dataset, creating / Create train and test datasets
  • modeling / Modeling
• HadCRUT4 annual time series
  • URL / Business understanding
• Hadoop
  • using / Parallel cloud computing with MapReduce and Hadoop
  • URL / Parallel cloud computing with MapReduce and Hadoop
• HadSST3 sea-surface datasets
  • URL / Business understanding
• Hannan-Quinn Criterion (HQ)
  • about / Examining the causality
• harmonic mean / The F-measure
• Hat Matrix / Modeling and evaluation
• header line
  • about / Importing and saving data from CSV files
• heatmaps / Data understanding and preparation
• heteroscedasticity / Business understanding
• hierarchical clustering
  • about / Hierarchical clustering
  • distance calculations / Distance calculations
  • modeling / Hierarchical clustering
  • evaluation / Hierarchical clustering
• histograms / Visualizing numeric variables – histograms
• holdout method / Cross-validation, The holdout method, Cross-validation
• Holt-Winter's Method
  • about / Univariate time series analysis
• httr package
  • URL / Downloading the complete text of web pages
• hybrid recommender engines / Understanding recommendation systems
• 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?
• Integrated Development Environment (IDE) / Delving into the basics of R
  • about / Getting R up and running
• intercept
  • about / Understanding regression
• International Space Station (ISS) / Topic modeling
• interquartile range
  • about / Hierarchical clustering
• Interquartile Range (IQR) / Measuring spread – quartiles and the five-number summary
• Item-Based Collaborative Filtering (IBCF) / Model preparation and prediction
• item-based collaborative filtering (IBCF)
  • about / Item-based collaborative filtering
• item-based collaborative filters / Understanding recommendation systems
• 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
  • about / Logistic regression with cross-validation
• k-fold cross-validation (or k-fold CV) / Cross-validation
• K-fold cross validation method / Cross-validation
• K-Means / K-Means
• k-means++ / Using distance to assign and update clusters
• k-means clustering
  • about / K-means clustering
  • modeling / K-means clustering
  • evaluation / K-means clustering
• k-means clustering algorithm
  • about / The k-means clustering algorithm
• K-Nearest Neighbors (KNN)
  • about / K-Nearest Neighbors (KNN), K-Nearest Neighbors
  • data, exploring / Collecting and exploring data
  • data, collecting / Collecting and exploring data
  • data, normalizing / Normalizing data
  • training and test data sets, creating / Creating training and test data sets
  • data/training model / Learning from data/training the model
  • model, evaluating / Evaluating the model
  • case study / Business case
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / KNN modeling
• 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
• K-sets
  • about / Logistic regression with cross-validation
• kernels
  • using, for non-linear spaces / Using kernels for non-linear spaces
• kernel trick / Using kernels for non-linear spaces
  • about / Support Vector Machines
• kernlab
  • reference / Step 3 – training a model on the data
• kNN collaborative filtering / The collaborative filtering algorithm
• KNN modeling
  • versus SVM modeling / Model selection
• Knowledge Discovery from Data (KDD) / Data mining @social networks
• Knowledge Mining / Data mining @social networks

L

• L1-norm
  • about / LASSO
• L2-norm
  • about / Ridge regression
• labeled tweets
  • URL / Labeled dataset
• Laplace estimator
  • about / The Laplace estimator
• lapply function / lapply and sapply
• 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
• LASSO
  • about / LASSO
  • executing / LASSO
• Latent Dirichlet Allocation (LDA)
  • about / Topic models
• latitude / Using kernels for non-linear spaces
• layers
  • about / The number of layers
• lazy learning
  • about / K-Nearest Neighbors
• 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-Cross-Validation (LOOCV)
  • about / Logistic regression with cross-validation
• Leave-One-Out Cross-Validation (LOOCV) / Modeling and evaluation
• leave-one-out method / Cross-validation
• left-hand side (LHS) / Core concepts and definitions, 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 Discriminant Analysis (QDA)
  • about / Discriminant analysis overview
• linear kernel / Using kernels for non-linear spaces
• linear model considerations
  • about / Other linear model considerations
  • qualitative feature / Qualitative feature
  • interaction term / Interaction term
• linear regression / Linear regression
  • about / Classification methods and linear regression
• linear regression model
  • linearity / Business understanding
  • non-correlation of errors / Business understanding
  • homoscedasticity / Business understanding
  • no collinearity / Business understanding
  • presence of outliers / Business understanding
• link function / Understanding regression
• lists
  • about / Lists
  • creating / Creating and indexing lists
  • indexing / Creating and indexing lists
  • combining / Combining and converting lists
  • converting / Combining and converting lists
  / Lists
• loess curve / Visualizing relationships among features – the scatterplot matrix
• logistic regression
  • used, for modeling / Modeling using logistic regression
  • about / Understanding regression, Logistic regression
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
  • with cross-validation / Logistic regression with cross-validation
  • Discriminant Analysis (DA) / Discriminant analysis overview
• logistic regression model
  • about / The logistic regression model
• longitude / Using kernels for non-linear spaces
• loss function
  • about / Gradient boosting

M

• machine learning
  • about / Machine learning basics, Machine learning – what does it really mean?, Understanding machine learning, The origins of machine learning
  • uses / Machine learning – how is it used in the world?, Uses and abuses of machine learning
  • algorithms, types / Types of machine learning algorithms
  • algorithms, supervised / Supervised machine learning algorithms
  • packages / Popular machine learning packages in R
  • algorithms / Algorithms in machine learning
  • perceptron / Perceptron
  • origins / The origins of machine learning
  • abuses / 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, Types of machine learning algorithms
  • supervised / Supervised machine learning algorithms
  • unsupervised / Unsupervised machine learning algorithms
  • about / Machine learning algorithms
  • linear classification algorithms / Machine learning algorithms
  • decision trees / Machine learning algorithms
  • ensemble learning methods / Machine learning algorithms
  • boosting algorithms / Machine learning algorithms
  • neural networks / Machine learning algorithms
• magrittr package
  • about / Scraping data from web pages
  • URL / Scraping data from web pages
• Mallow's Cp (Cp) / Modeling and evaluation
• mapply function / mapply
• 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
• margin
  • about / Support Vector Machines
• marginal likelihood
  • about / Computing conditional probability with Bayes' theorem
• market basket analysis
  • about / Detecting and predicting trends, Market basket analysis, What does market basket analysis actually mean?, An overview of a market basket analysis
  • core concepts / Core concepts and definitions
  • techniques / Techniques used for analysis
  • data driven decisions, making / Making data driven decisions
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
• 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
• matrices
  • creating / Data frames and matrices
• matrix
  • about / Matrixes and arrays
  / Matrixes and arrays
• matrix factorization / Building a recommender engine, Matrix factorization
• matrix notation / Multiple linear regression
• matrix operations
  • about / Matrix operations
• maximum margin hyperplane (MMH) / Classification with hyperplanes
• mean / Measuring the central tendency – mean and median
• mean absolute error (MAE) / Model evaluation, Measuring performance with the mean absolute error
• mean squared error (MSE) / Model evaluation
  • about / Best subsets, Univariate time series analysis
• 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
• medoid
  • about / PAM
• 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
• miss rate / Evaluating predictive models
• 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
  • evaluating / Predictive analytics
  • tuning / Predictive analytics
  • deployment / Predictive analytics
• modeling
  • logistic regression used / Modeling using logistic regression
  • support vector machines used / Modeling using support vector machines
  • decision trees used / Modeling using decision trees
  • random forests used / Modeling using random forests
  • neural networks used / Modeling using neural networks
  • comparision / Model comparison and selection
  • selecting / Model comparison and selection
• modeling process
  • about / Modeling
• 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 Adaptive Regression Splines (MARS) / Linear regression
• multivariate linear regression
  • about / Multivariate linear regression
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
• multivariate relationships
  • about / Exploring relationships between variables

N

• n-Grams / Feature extraction
  • URL / Feature extraction
• 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
• names and dimensions
  • about / Names and dimensions
• natural language processing (NLP) problem / Challenges
• nearest neighbor classification
  • about / Understanding nearest neighbor classification
• negation / Feature extraction
• negative predictive value / Evaluating predictive models
• neighbour-based collaborative filtering / The collaborative filtering algorithm
• 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 network
  • about / Neural network
  • business understanding / Business understanding
  • reference link / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
• neural networks
  • used, for modeling / Modeling using 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
• Normal Q-Q plot / Business understanding
• NPV / Modeling using logistic regression
• 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 Authentication (OAuth) protocol / Registering the application
• Open Database Connectivity (ODBC)
  • about / Querying data in SQL databases
• OPRC / Modeling and evaluation
• OPSLAKE / Modeling and evaluation
• 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 (oob)
  • about / Random forest
• out-of-bag error rate / Training random forests
• out of bag error (OOBE) / Modeling using random forests
• overfitting / Evaluation

P

• 2p models
  • about / Regularization in a nutshell
• 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
• Partial Autocorrelation Function (PACF)
  • about / Univariate time series analysis
• Partitioning Around Medoids (PAM)
  • about / Gower and partitioning around medoids, PAM
• Parts of Speech (POS) / Feature extraction
• pattern discovery / Types of machine learning algorithms
• Pearson's correlation coefficient / Correlations
• Pearson correlation / Similarity
• Pearson Correlation Coefficient
  • about / User-based collaborative filtering
• perceptron / Perceptron
• 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
• pixel-oriented maps / Pixel-oriented maps
• 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
• Polarity / Polarity analysis
  • about / Other quantitative analyses
• polynomial kernel / Using kernels for non-linear spaces
• Porter stemming algorithm
  • about / Text mining framework and methods
• positive predictive value / Evaluating predictive models, 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 / Evaluating predictive models, Precision and recall
• Prediction Error Sum of Squares (PRESS) / Modeling and evaluation
• prediction operation / Core concepts and definitions
• predictive analytics / Types of analytics, Our next challenge
  • about / Predictive analytics
• predictive model / Types of machine learning algorithms
• predictive modeling
  • about / Predictive analytics, Important concepts in predictive modeling
  • data, preparing / Preparing the data
  • datasets / Preparing the data
  • data, observations / Preparing the data
  • data, features / Preparing the data
  • data, transformation / Preparing the data
  • training data / Preparing the data
  • data, training / Preparing the data
  • data, testing / Preparing the data
  • predictive models, building / Building predictive models
  • predictive models, evaluating / Evaluating predictive models
• predictive models, building
  • about / Building predictive models
  • model training / Building predictive models
  • predictive model / Building predictive models
  • model, selecting / Building predictive models
  • hyperparameter optimization / Building predictive models
  • cross validation / Building predictive models
• predictive models, evaluating
  • about / Evaluating predictive models
  • prediction values / Evaluating predictive models
  • confusion matrix / Evaluating predictive models
• prescriptive analytics / Types of analytics
• Principal Component Analysis (PCA) / Model preparation and prediction
• principal components
  • overview / An overview of the principal components
  • rotation / Rotation
• Principal Components Analysis (PCA)
  • about / Gower and partitioning around medoids
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
  • component, extraction / Component extraction
  • orthogonal rotation / Orthogonal rotation and interpretation
  • interpretation / Orthogonal rotation and interpretation
  • factor scores, creating from components / Creating factor scores from the components
  • regression analysis / Regression analysis
• principal components analysis (PCA)
  • about / Singular value decomposition and principal components analysis
• prior probability
  • about / Computing conditional probability with Bayes' theorem
• probability
  • about / Understanding probability
• product contingency matrix
  • evaluating / Evaluating a product contingency matrix
  • data, getting / Getting the data
  • data, analyzing / Analyzing and visualizing the data
  • data, visualizing / Analyzing and visualizing the data
  • global recommendations / Global recommendations
  • advanced / Advanced contingency matrices
• 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 Discriminant Analysis (QDA)
  • about / Discriminant analysis overview
• quadratic optimization / The case of linearly separable data
• Quantile-Quantile (Q-Q) / Business understanding
• quantiles / Measuring spread – quartiles and the five-number summary
• quartimax
  • about / Rotation

R

• 1 R algorithm / The 1R algorithm
• R
  • basics / Delving into the basics of R
  • using, as scientific calculator / Using R as a scientific calculator
  • vectors, operating on / Operating on vectors
  • special values / Special values
  • data structures / Data structures in R, R data structures
  • next steps / Next steps with R
  • help / Getting help
  • packages, handling / Handling packages
  • about / Machine learning with R
  • packages, installing / Installing R packages
  • packages, loading / Loading and unloading R packages
  • packages, unloading / Loading and unloading R packages
  • used, for managing data / Managing data with R
  • working with classification prediction data / Working with classification prediction data in R
  • installing / Getting R up and running
  • running / Getting R up and running
  • URL / Getting R up and running
  • using / Using 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) / Modeling using support vector machines
• Radial Basis Function (RBF) network
  • about / Activation functions
• radial bias kernel (rbf) / Support Vector Machines
• radical
  • about / Text mining framework and methods
• random forest
  • about / Introduction
  • overview / Random forest
  • business case / Business case
  • model selection / Model selection
• random forest classification
  • modeling / Random forest classification
  • evaluation / Random forest classification
• random forest regression
  • evaluation / Random forest regression
  • modeling / Random forest regression
• random forests
  • used, for modeling / Modeling using 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
• rate of descent / Matrix factorization
• ratings matrix / Core concepts and definitions
• RCurl
  • URL / Downloading the complete text of web pages
• Read-Evaluate-Print Loop (REPL) / Delving into the basics of R
• rea under the ROC curve (AUC) / ROC curves
• Receiver Operating Characteristic (ROC)
  • about / Model selection
  • reference link / Model selection
• Receiver Operating Characteristic (ROC) curve
  • about / ROC curves
  • creating / ROC curves
• Receiver Operating Characteristic Curves (ROC)
  • about / Modeling, evaluation, and recommendations
• Receiver Operator Characteristic (ROC) curve / Evaluating predictive models
• recommendation engine
  • overview / An overview of a recommendation engine
  • collaborative filtering / An overview of a recommendation engine
  • business understanding / Business understanding and recommendations
  • data, understanding / Data understanding, preparation, and recommendations
  • data, preparing / Data understanding, preparation, and recommendations
  • modeling / Modeling, evaluation, and recommendations
  • evaluation / Modeling, evaluation, and recommendations
  • recommendations / Modeling, evaluation, and recommendations
• recommendation systems
  • offline-recommender engines / Understanding recommendation systems
  • online-recommender engines / Understanding recommendation systems
  • types / Understanding recommendation systems
  • issues / Issues with recommendation systems
• recommendation systems, issues
  • sparsity problem / Issues with recommendation systems
  • cold start problem / Issues with recommendation systems
• recommendation systems, product ready
  • about / Production ready recommender engines
• recommendation systems, types
  • user-based recommender engines / Understanding recommendation systems
  • content-based recommender engines / Understanding recommendation systems
  • hybrid recommender engines / Understanding recommendation systems
• recommender engine
  • building / Building a recommender engine
  • matrix factorization / Matrix factorization
  • implementing / Implementation
  • result interpretation / Result interpretation
• recommender engines
  • production ready / Production ready recommender engines
  • extract, transform, and analyze / Extract, transform, and analyze
  • model, preparation and prediction / Model preparation and prediction
  • model evaluation / Model evaluation
• recommenderlab / Production ready recommender engines
• recommenderlab library
  • URL / Data understanding, preparation, and recommendations
• recommend operation / Core concepts and definitions
• recurrent network
  • about / The direction of information travel
• recursive partitioning
  • about / Divide and conquer
• references / References
• 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
  • overview / Regression trees
  • business case / Business case
  • modeling / Regression tree
  • evaluation / Regression tree
• regularization / Matrix factorization
  • about / Regularization in a nutshell
  • ridge regression / Ridge regression
  • LASSO / LASSO
  • elastic net / Elastic net
  • business case / Business case
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
  • cross-validation, performing with glmnet package / Cross-validation with glmnet
  • model selection / Model selection
• regularization, modeling
  • best subsets, creating / Best subsets
  • ridge regression / Ridge regression
  • LASSO, running / LASSO
  • elastic net, using / Elastic net
• 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
• Residual Sum of Squares (RSS) / Univariate linear regression
• Residuals vs Leverage plot / Business understanding
• Restricted Boltzmann Machine
  • about / Deep learning, a not-so-deep overview
• 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
• ridge regression
  • about / Ridge regression
  • executing / Ridge regression
• right-hand side (RHS) / Core concepts and definitions
• 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
• root mean squared error (RMSE) / Model evaluation
• Root Mean Square Error (RMSE)
  • about / Elastic net
• Root Mean Square Error/RMSE) / Cross-validation
• rotational estimation / Cross-validation
• rote learning
  • about / Why is the k-NN algorithm lazy?
• R packages
  • installing / Installing and loading the R packages
  • loading / Installing and loading the R packages
• rpart.plot
  • URL / Visualizing decision trees
• RStudio
  • URL / Getting R up and running
• RTextTools / Cross-validation
• 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
• Schwarz-Bayes Criterion (SC)
  • about / Examining the causality
• Scoville scale / Preparing data for use with k-NN
• second principal component
  • about / An overview of the principal components
• segmentation analysis / Types of machine learning algorithms
• semi-supervised learning / Clustering as a machine learning task
• sensitivity / Evaluating predictive models
• sentiment analysis
  • about / Understanding Sentiment Analysis
  • key concepts / Key concepts of sentiment analysis
  • subjectivity / Subjectivity
  • sentiment polarity / Sentiment polarity
  • opinion summarization / Opinion summarization
  • feature extraction / Feature extraction
  • approaches / Approaches
  • applications / Applications
  • challenges / Challenges
  • upon Tweets / Sentiment analysis upon Tweets
  • polarity analysis / Polarity analysis
  • classification-based algorithms / Classification-based algorithms
  • labeled dataset / Labeled dataset
  • Support Vector Machines (SVM) / Support Vector Machines
  • ensemble methods / Ensemble methods
  • boosting / Boosting
  • cross-validation / Cross-validation
• sentiment analysis, abstraction
  • document level / Approaches
  • sentence level / Approaches
• separate and conquer
  • about / Separate and conquer
• shrinkage penalty
  • about / Regularization in a nutshell
• 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
• singular value decomposition (SVD)
  • about / Singular value decomposition and principal components analysis
• slack variable / The case of nonlinearly separable data
• slack variables
  • about / Support Vector Machines
• 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 network data mining
  • challenges / Challenges with social network data mining
• social networking service (SNS) / Example – finding teen market segments using k-means clustering
• social networks
  • about / Social networks (Twitter)
• Sparse Coding Model
  • about / Deep learning, a not-so-deep overview
• sparse matrix / Core concepts and definitions, Data preparation – splitting text documents into words, Data preparation – creating a sparse matrix for transaction data
• Spearman rank correlation / Similarity
• specificity / Evaluating predictive models
• SQL databases
  • data, querying in / Querying data in SQL databases
• squared error / Matrix factorization
• 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
• summary stats
  • displaying / Summary stats
• Sum of Squared Error
  • about / Neural network
• sum of squared error (SSE)
  • about / Univariate time series analysis
• supervised learning / Types of machine learning algorithms
• supervised learning algorithms
  • about / Supervised learning algorithms
  • problems / Supervised learning algorithms
  • regression based machine learning / Supervised learning algorithms
  • classification based machine learning / Supervised learning algorithms
• supervised machine learning algorithms
  • about / Supervised machine learning algorithms
  • regression algorithms / Supervised 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 vector machines
  • used, for modeling / Modeling using support vector machines
• Support Vector Machines (SVM) / Supervised learning algorithms, Approaches, Support Vector Machines
  • about / Support Vector Machines
  • case study / Business case
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / SVM modeling
  • feature selection / Feature selection for SVMs
• support vectors / Classification with hyperplanes
• suspected outliers
  • about / Hierarchical clustering
• SVMlight
  • about / Step 3 – training a model on the data
  • URL / Step 3 – training a model on the data
• SVM modeling
  • versus KNN modeling / Model selection
• synapse
  • about / From biological to artificial neurons

T

• Tab-Separated Value (TSV)
  • about / Importing and saving data from CSV files
• Tableau specific
  • URL / References
• tabular
  • about / Importing and saving data from CSV files
• tabular data structures
  • generalizing, with dplyr package / Generalizing tabular data structures with dplyr
• tag clouds / Word clouds
• tapply function / tapply
• 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
• Term-Document Matrix (TDM)
  • about / Text mining framework and methods
• Term Frequency-Inverse Document Frequency (tf-idf) / Feature extraction
• terminal nodes / Understanding decision trees
• text mining
  • methods / Text mining framework and methods
  • topic models / Topic models
  • other quantitative analyses / Other quantitative analyses
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
  • word frequency, exploring / Word frequency and topic models
  • topic models, building / Word frequency and topic models
  • quantitative analysis, with qdap package / Additional quantitative analysis
• threshold activation function / Activation functions
• topic models
  • about / Topic models
• training / Abstraction
• tree-based learning
  • about / Gradient boosting
• Treemaps / Treemaps
• trees
  • regression, adding to / Adding regression to trees
• tree structure
  • about / Understanding decision trees
• trends
  • detecting / Detecting and predicting trends
  • predicting / Detecting and predicting trends
• True Negative (TN) / Evaluating predictive models
• true negative rate / Evaluating predictive models
• True Positive (TP) / Evaluating predictive models
• true positive rate / Evaluating predictive models
• True Positive Rate (TPR)
  • about / Model selection
• tuning process
  • customizing / Customizing the tuning process
• Twitter
  • about / Social networks (Twitter)
  • Best Practices, URL / Overview
• Twitter APIs
  • about / Getting started with Twitter APIs, Overview
  • URL / Overview
  • application, registering / Registering the application
  • connect/authenticate / Connect/authenticate
  • sample tweets, extracting / Extracting sample tweets
• Twitter data mining
  • about / Twitter data mining
  • frequent words and associations / Frequent words and associations
  • popular devices / Popular devices
  • hierarchical clustering revisited / Hierarchical clustering
  • topic modeling / Topic modeling
• 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 linear regression
  • about / Univariate linear regression
  • business understanding / Business understanding
• univariate statistics
  • about / Exploring relationships between variables
• univariate time series
  • analyzing / Univariate time series analysis
  • with bivariate regression / Bivariate regression
  • analyzing, with Granger causality / Granger causality
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
  • forecasting / Univariate time series forecasting
  • examining, with regression / Time series regression
  • Granger causality, examining / Examining the causality
• universal function approximator / The number of nodes in each layer
• unsupervised learning / Types of machine learning algorithms
• unsupervised learning algorithms
  • about / Unsupervised learning algorithms
  • association rule based machine learning / Unsupervised learning algorithms
  • clustering based machine learning / Unsupervised learning algorithms
  • Apriori algorithm / Apriori algorithm
  • K-Means / K-Means
• unsupervised machine learning algorithms
  • about / Unsupervised machine learning algorithms
  • clustering algorithms / Unsupervised machine learning algorithms
  • associate rule learning algorithms / Unsupervised machine learning algorithms
• user-based collaborative filtering (UBCF)
  • about / User-based collaborative filtering
• user-based recommender engines / Understanding recommendation systems
• user-user collaborative filtering / The collaborative filtering algorithm

V

• valence shifters
  • about / Other quantitative analyses
• Variance Inflation Factor (VIF) / Modeling and evaluation
• varimax
  • about / Rotation
• vector
  • about / Vectors
• Vector Autoregression (VAR)
  • about / Granger causality
• vectors
  • about / Vectors
  • creating / Creating vectors
  • indexing / Indexing and naming vectors
  • naming / Indexing and naming vectors
• vector types
  • types / Vectors
• visualizations / Other visualizations
• 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
• weighted average / Predictions
• whiskers
  • about / Hierarchical clustering
• 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
• word clouds / 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