Index

A

• Aikake's Information Criterion (AIC) / Modeling and evaluation
  • about / Granger causality
• algorithm flowchart
  • about / Algorithm flowchart
• American Diabetes Association (ADA)
  • URL / Business understanding
• apriori algorithms
  • about / An overview of a market basket analysis
• Area Under the Curve (AUC)
  • about / Model selection
• 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
• Augmented Dickey-Fuller (ADF) test
  • about / Data understanding and preparation
• Autocorrelation Function (ACF)
  • about / Univariate time series analysis
• Autoregressive Integrated Moving Average (ARIMA) models
  • about / Univariate time series analysis

B

• Back Propagation
  • about / Neural network
• backward stepwise regression / Modeling and evaluation
• bagging
  • about / Random forest
• Bayesian Information Criterion (BIC) / Modeling and evaluation
• bias-variance trade-off
  • about / Discriminant analysis overview
• bivariate regression
  • for univariate time series / Bivariate regression
• bootstrap aggregation
  • about / Random forest
• 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

• Carbon Dioxide Information Analysis Center (CDIAC)
  • URL / Business understanding
• caret package
  • about / Elastic net
  • URL / Elastic net
• classification methods
  • about / Classification methods and linear regression
• classification models
  • selecting / Model selection
• classification trees
  • overview / Classification trees
  • business case / Business case
  • evaluation / Classification tree
  • modeling / Classification tree
• 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
• 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
• Cook's distance / Business understanding
• Corpus
  • about / Text mining framework and methods
• Cosine Similarity
  • about / User-based collaborative filtering
• 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
  • for logistic regression / Logistic regression with cross-validation
• Cross Correlation Function (CCF)
  • about / Data understanding and preparation
• CRUTEM4 surface air temperature
  • URL / Business understanding

D

• data frame
  • creating / Data frames and matrices
• data preparation process
  • about / Data preparation
• data understanding process
  • about / Data understanding
• deep learning
  • 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
• deployment process / Deployment
• 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
• Document-Term Matrix (DTM)
  • about / Text mining framework and methods
• dynamic topic modelling
  • about / Topic models

E

• ECLAT algorithms
  • about / An overview of a market basket analysis
• eigenvalues
  • about / An overview of the principal components
• eigenvectors
  • about / An overview of the principal components
• elastic net
  • about / Elastic net
  • using / Elastic net
• equimax
  • about / Rotation
• Euclidian Distance
  • about / K-Nearest Neighbors
• evaluation process
  • about / Evaluation
• exponential smoothing models
  • about / Univariate time series analysis
• Extract, Transport, and Load (ETL)
  • about / Data understanding

F

• F-Measure
  • about / Other quantitative analyses
• False Positive Rate (FPR)
  • about / Model selection
• Feed Forward network
  • about / Neural network
• Final Prediction Error (FPE)
  • about / Granger causality
• Fine Needle Aspiration (FNA)
  • about / Business understanding
• first principal component
  • about / An overview of the principal components
• forward stepwise selection / Modeling and evaluation

G

• Gedeon Method
  • about / Modeling
• glmnet package
  • used, for performing cross-validation for regularization / Cross-validation with glmnet
• Gower
  • about / Gower and partitioning around medoids
• 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
• Granger causality
  • about / Granger causality
• Graphical User Interface (GUI)
  • about / Getting R up and running

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
• HadSST3 sea-surface datasets
  • URL / Business understanding
• Hannan-Quinn Criterion (HQ)
  • about / Examining the causality
• Hat Matrix / Modeling and evaluation
• heatmaps / Data understanding and preparation
• heteroscedasticity / Business understanding
• hierarchical clustering
  • about / Hierarchical clustering
  • distance calculations / Distance calculations
  • modeling / Hierarchical clustering
  • evaluation / Hierarchical clustering
• Holt-Winter's Method
  • about / Univariate time series analysis

I

• Integrated Development Environment (IDE)
  • about / Getting R up and running
• interquartile range
  • about / Hierarchical clustering
• item-based collaborative filtering (IBCF)
  • about / Item-based collaborative filtering

K

• K-fold cross-validation
  • about / Logistic regression with cross-validation
• k-means clustering
  • about / K-means clustering
  • modeling / K-means clustering
  • evaluation / K-means clustering
• K-Nearest Neighbors (KNN)
  • about / K-Nearest Neighbors
  • case study / Business case
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / KNN modeling
• K-sets
  • about / Logistic regression with cross-validation
• kernel trick
  • about / Support Vector Machines
• KNN modeling
  • versus SVM modeling / Model selection

L

• L1-norm
  • about / LASSO
• L2-norm
  • about / Ridge regression
• LASSO
  • about / LASSO
  • executing / LASSO
• Latent Dirichlet Allocation (LDA)
  • about / Topic models
• lazy learning
  • about / K-Nearest Neighbors
• Leave-One-Out-Cross-Validation (LOOCV)
  • about / Logistic regression with cross-validation
• Leave-One-Out Cross-Validation (LOOCV) / Modeling and evaluation
• Linear Discriminant Analysis (QDA)
  • about / Discriminant analysis overview
• linear model considerations
  • about / Other linear model considerations
  • qualitative feature / Qualitative feature
  • interaction term / Interaction term
• 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
• logistic regression
  • about / 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
• loss function
  • about / Gradient boosting

M

• Mallow's Cp (Cp) / Modeling and evaluation
• margin
  • about / Support Vector Machines
• market basket analysis
  • about / An overview of a market basket analysis
  • business understanding / Business understanding
  • data, understanding / Data understanding and preparation
  • data, preparing / Data understanding and preparation
  • modeling / Modeling and evaluation
  • evaluation / Modeling and evaluation
• matrices
  • creating / Data frames and matrices
• mean squared error (MSE)
  • about / Best subsets, Univariate time series analysis
• medoid
  • about / PAM
• modeling process
  • about / Modeling
• 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

N

• 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
• Normal Q-Q plot / Business understanding

O

• OPRC / Modeling and evaluation
• OPSLAKE / Modeling and evaluation
• out-of-bag (oob)
  • about / Random forest

P

• 2p models
  • about / Regularization in a nutshell
• Partial Autocorrelation Function (PACF)
  • about / Univariate time series analysis
• Partitioning Around Medoids (PAM)
  • about / Gower and partitioning around medoids, PAM
• Pearson Correlation Coefficient
  • about / User-based collaborative filtering
• Polarity
  • about / Other quantitative analyses
• Porter stemming algorithm
  • about / Text mining framework and methods
• Prediction Error Sum of Squares (PRESS) / Modeling and evaluation
• principal components
  • overview / An overview of the principal components
  • rotation / Rotation
• principal components analysis (PCA)
  • about / Singular value decomposition and principal components analysis
• 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

Q

• Quadratic Discriminant Analysis (QDA)
  • about / Discriminant analysis overview
• Quantile-Quantile (Q-Q) / Business understanding
• quartimax
  • about / Rotation

R

• R
  • installing / Getting R up and running
  • running / Getting R up and running
  • URL / Getting R up and running
  • using / Using R
• 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
• Receiver Operating Characteristic (ROC)
  • about / Model selection
  • reference link / Model selection
• Receiver Operating Characteristic Curves (ROC)
  • about / Modeling, evaluation, and recommendations
• 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
• recommenderlab library
  • URL / Data understanding, preparation, and recommendations
• regression trees
  • overview / Regression trees
  • business case / Business case
  • modeling / Regression tree
  • evaluation / Regression tree
• regularization
  • 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
• 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
• ridge regression
  • about / Ridge regression
  • executing / Ridge regression
• Root Mean Square Error (RMSE)
  • about / Elastic net
• R packages
  • installing / Installing and loading the R packages
  • loading / Installing and loading the R packages
• RStudio
  • URL / Getting R up and running

S

• Schwarz-Bayes Criterion (SC)
  • about / Examining the causality
• second principal component
  • about / An overview of the principal components
• shrinkage penalty
  • about / Regularization in a nutshell
• singular value decomposition (SVD)
  • about / Singular value decomposition and principal components analysis
• slack variables
  • about / Support Vector Machines
• Sparse Coding Model
  • about / Deep learning, a not-so-deep overview
• summary stats
  • displaying / Summary stats
• Sum of Squared Error
  • about / Neural network
• sum of squared error (SSE)
  • about / Univariate time series analysis
• Support Vector Machines (SVM)
  • 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
• suspected outliers
  • about / Hierarchical clustering
• SVM modeling
  • versus KNN modeling / Model selection

T

• Term-Document Matrix (TDM)
  • about / Text mining framework and methods
• 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
• topic models
  • about / Topic models
• tree-based learning
  • about / Gradient boosting
• True Positive Rate (TPR)
  • about / Model selection

U

• univariate linear regression
  • about / Univariate linear regression
  • business understanding / Business understanding
• 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
• user-based collaborative filtering (UBCF)
  • about / User-based collaborative filtering

V

• valence shifters
  • about / Other quantitative analyses
• Variance Inflation Factor (VIF) / Modeling and evaluation
• varimax
  • about / Rotation
• Vector Autoregression (VAR)
  • about / Granger causality

W

• whiskers
  • about / Hierarchical clustering