Index

A

• ACF function
  • about / Time series summary functions
• activators
  • about / The biological neuron
• acyclic graph
  • about / A little graph theory
• AdaBoost / AdaBoost
• AdaBoost, for binary classification
  • inputs / AdaBoost
  • output / AdaBoost
  • observations / AdaBoost
• adaptive boosting
  • about / AdaBoost
• additive smoothing
  • about / Predicting the sentiment of movie reviews
• Akaike Information Criterion (AIC)
  • about / Comparing different regression models
• algorithms
  • building, to train decision trees / Algorithms for training decision trees
• analysis of variance
  • about / Significance tests for linear regression
• ARCH models / Autoregressive conditional heteroscedasticity models
• ARIMA models / Autoregressive integrated moving average models
• ARMA model / Autoregressive moving average models
• artificial neural networks (ANNs)
  • about / The biological neuron
• artificial neuron
  • about / The artificial neuron
• atmospheric gamma ray radiation
  • predicting / Predicting atmospheric gamma ray radiation
• Augmented Dickey-Fuller (ADF) test
  • about / Autoregressive integrated moving average models
• authenticity, of banknotes
  • predicting / Predicting the authenticity of banknotes
• author-topic model
  • about / LDA extensions
• autocorrelation function
  • about / Time series summary functions
• autocovariance function
  • about / Time series summary functions
• autoregressive models (AR) / Autoregressive models
• axon
  • about / The biological neuron
• axon terminals
  • about / The biological neuron

B

• backpropagation algorithm
  • about / Training multilayer perceptron networks
• backward elimination
  • about / Feature selection
• backward selection
  • about / Feature selection
• bagging
  • about / Bagging
  • margin / Margins and out-of-bag observations
  • out-of-bag observations / Margins and out-of-bag observations
  • complex skill learning, predicting with / Predicting complex skill learning with bagging
  • heart disease, predicting with / Predicting heart disease with bagging
  • limitations / Limitations of bagging
• bagging, for binary classification
  • inputs / Bagging
  • output / Bagging
  • method / Bagging
• Banknote Authentication data set
  • URL / Predicting the authenticity of banknotes
• batch machine learning model / Real-time and batch machine learning models
• Baum-Welch algorithm
  • about / Hidden Markov models
• Bayesian Information Criterion (BIC)
  • about / Comparing different regression models
• Bayesian networks
  • defining / Bayesian networks
• Bayesian probability
  • about / Learning from data
• Bayes Theorem
  • defining / Bayes' Theorem
• bias
  • about / The biological neuron
• Big Data
  • handling, in R / R and Big Data
  • about / R and Big Data
• binary classification models
  • assessing / Assessing binary classification models
• biological neuron
  • about / The biological neuron
• boosting
  • about / Boosting, Limitations of boosting
  • AdaBoost / AdaBoost
  • limitations / Limitations of boosting
• bootstrapped samples
  • about / Margins and out-of-bag observations
• bootstrapping
  • about / Bagging
• bootstrap resampling
  • about / Bagging
• bootstrap sampling
  • about / Bagging
• Box-Cox transformation / Feature transformations
• Brownian Motion
  • about / Random walk

C

• C5.0 algorithm
  • about / C5.0
• caret package
  • about / Feature transformations
  • URL / Feature transformations, Assessing classification models
• CART classification trees
  • about / CART classification trees
• CART methodology
  • about / Classification and regression trees
  • CART regression trees / CART regression trees
  • tree pruning / Tree pruning
  • missing data / Missing data
• CART regression trees / CART regression trees
• categorical features
  • encoding / Encoding categorical features
• characteristic polynomial
  • about / Moving average models
• chemical biodegradation
  • predicting / Predicting chemical biodegration
• CHI squared
  • about / Model deviance
• classification metrics
  • about / Classification metrics
• classification model / Regression and classification models
• classification models
  • assessing / Assessing classification models
  • binary classification models, assessing / Assessing binary classification models
• class membership
  • predicting, on synthetic 2D data / Predicting class membership on synthetic 2D data
• clustering
  • about / Supervised, unsupervised, semi-supervised, and reinforcement learning models
  • benefits / Supervised, unsupervised, semi-supervised, and reinforcement learning models
• coefficients
  • interpreting, in logistic regression / Interpreting coefficients in logistic regression
• collaborative filtering
  • about / Collaborative filtering
  • user-based collaborative filtering / User-based collaborative filtering
  • item-based collaborative filtering / Item-based collaborative filtering
• complex skill learning
  • predicting, with boosting / Predicting complex skill learning with boosting
• complex skills
  • predicting / Predicting complex skill learning
• conditional independence
  • defining / Conditional independence
• confidence interval
  • about / Significance tests for linear regression
• confusion matrix
  • about / Assessing classification models
• Correlated Topic Model (CTM)
  • about / The generative process, Modeling the topics of online news stories
• correlogram
  • about / Fitting a white noise time series
• cost-complexity tuning
  • about / Tree pruning
• cost function
  • about / Stochastic gradient descent
• CPU performance
  • predicting / Predicting CPU performance
• credit scores
  • predicting / Predicting credit scores
• cross-validation
  • about / Feature transformations, Cross-validation
• cycle
  • about / A little graph theory

D

• data, pre-processing
  • about / Preprocessing the data
  • exploratory data analysis / Exploratory data analysis
  • feature transformations / Feature transformations
  • categorical features, encoding / Encoding categorical features
  • data, missing value / Missing data
  • outliers / Outliers
  • problematic features, removing / Removing problematic features
• data, recommendation systems
  • loading / Loading and preprocessing the data
  • preprocessing / Loading and preprocessing the data
  • exploring / Exploring the data
  • binary top-N recommendations, evaluating / Evaluating binary top-N recommendations
  • non-binary top-N recommendations, evaluating / Evaluating non-binary top-N recommendations
  • individual predictions, evaluating / Evaluating individual predictions
• data columns
  • about / Predicting CPU performance
• data set
  • LDA_VEM / Modeling the topics of online news stories
  • LDA_VEM_? / Modeling the topics of online news stories
  • LDA_GIB / Modeling the topics of online news stories
  • CTM_VEM / Modeling the topics of online news stories
• data sets
  • URL / Modeling the topics of online news stories
• decision tree models
  • about / The intuition for tree models
• decision trees
  • training, via building algorithms / Algorithms for training decision trees
• dendrites
  • about / The biological neuron
• deviance
  • about / Model deviance
• dimensionality reduction / Feature engineering and dimensionality reduction
• directed acyclic graph (DAG)
  • about / A little graph theory
• directed graph
  • about / A little graph theory
• Dirichlet distribution / The Dirichlet distribution
• Discrete AdaBoost
  • about / AdaBoost
• discrete white noise
  • about / White noise
• document term matrix
  • about / Predicting the sentiment of movie reviews
• dynamic programming
  • about / Hidden Markov models

E

• Ecotect / Predicting the energy efficiency of buildings
• emitted symbol
  • about / Hidden Markov models
• energy efficiency, of buildings
  • predicting / Predicting the energy efficiency of buildings
• Energy Efficiency data set
  • URL / Predicting the energy efficiency of buildings
• entropy
  • about / C5.0
• expectation
  • about / Estimating the regression coefficients
• Expectation Maximization (EM) algorithm
  • about / Fitting an LDA model
• exploratory data analysis / Exploratory data analysis
• exponential smoothing
  • about / Other time series models
• extensions, binary logistic classifier
  • about / Extensions of the binary logistic classifier
  • multinomial logistic regression / Multinomial logistic regression
  • ordinal logistic regression / Ordinal logistic regression

F

• false negative
  • about / Assessing binary classification models
• false positive
  • about / Assessing binary classification models
• false positive rate (FPR) / Evaluating binary top-N recommendations
• feature engineering / Feature engineering and dimensionality reduction
• features
  • selecting / Feature selection
• features, data set
  • about / Predicting complex skill learning
• features, used cars
  • Price / Predicting the price of used cars
  • Mileage / Predicting the price of used cars
  • Cylinder / Predicting the price of used cars
  • Doors / Predicting the price of used cars
  • Cruise / Predicting the price of used cars
  • Sound / Predicting the price of used cars
  • Leather / Predicting the price of used cars
  • Buick / Predicting the price of used cars
  • Cadillac / Predicting the price of used cars
  • Chevy / Predicting the price of used cars
  • Pontiac / Predicting the price of used cars
  • Saab / Predicting the price of used cars
  • Saturn / Predicting the price of used cars
  • convertible / Predicting the price of used cars
  • coupe / Predicting the price of used cars
  • hatchback / Predicting the price of used cars
  • sedan / Predicting the price of used cars
  • wagon / Predicting the price of used cars
• feature transformations / Feature transformations
• feedforward neural network
  • about / Multilayer perceptron networks
• foreign exchange rates
  • predicting / Predicting foreign exchange rates
• forward selection
  • about / Feature selection
• frequency domain methods
  • about / Other time series models
• Frequentist probability
  • about / Learning from data
• F statistic
  • about / Significance tests for linear regression

G

• gamma function
  • about / The Dirichlet distribution
• GARCH models / Generalized autoregressive heteroscedasticity models
• generalized linear models (GLMs) / Generalized linear models
• generative models
  • about / Predicting letter patterns in English words
• generative process
  • about / The generative process
  • working / The generative process
• German Credit Dataset
  • about / Predicting credit scores
  • URL / Predicting credit scores
• Gini index
  • calculating / CART classification trees
• glass type revisited
  • predicting / Predicting glass type revisited
• gradient descent
  • about / Stochastic gradient descent
• graphical models
  • about / A little graph theory
• graphs
  • about / A little graph theory
• graph theory
  • about / A little graph theory

H

• handwritten digits
  • predicting / Predicting handwritten digits
  • URL / Predicting handwritten digits
  • ROC curve / Receiver operating characteristic curves
• HMM
  • defining / Hidden Markov models
• hyperplanes
  • about / Maximal margin classification
  • property / Maximal margin classification

I

• ID3
  • about / C5.0
• Independence of Irrelevant Alternatives (IIA)
  • about / Multinomial logistic regression
• independent and identically distributed (iid)
  • about / Margins and out-of-bag observations, White noise
• information statistic
  • about / C5.0
• inhibitors
  • about / The biological neuron
• inner products / Inner products
• intense earthquakes
  • predicting / Predicting intense earthquakes
• intercept
  • about / Introduction to linear regression
• interquartile range
  • about / Residual analysis
• invertible
  • about / Moving average models
• item-based collaborative filtering
  • about / Item-based collaborative filtering

J

• J48
  • about / C5.0

K

• k-fold cross-validation
  • about / Cross-validation
• k-nearest neighbors / Our first model: k-nearest neighbors
• Kappa statistic
  • defining / Assessing classification models
• kernel functions
  • about / Kernels and support vector machines
• kernels
  • about / Kernels and support vector machines
  • using / Kernels and support vector machines
• kNN
  • about / Our first model: k-nearest neighbors
• Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test
  • about / Autoregressive integrated moving average models

L

• laplacian smoothing
  • about / Predicting the sentiment of movie reviews
• LDA
  • defining / Latent Dirichlet Allocation
  • Dirichlet distribution / The Dirichlet distribution
  • generative process / The generative process
  • model, fitting / Fitting an LDA model
• LDA extensions / LDA extensions
• LDA model
  • fitting / Fitting an LDA model
  • training / Modeling the topics of online news stories
• least absolute shrinkage / Least absolute shrinkage and selection operator (lasso)
• letter patterns
  • predicting, in English words / Predicting letter patterns in English words
• Likert scale
  • about / Ordinal logistic regression
• linear kernel
  • about / Kernels and support vector machines
• linear regression
  • about / Introduction to linear regression
  • assumptions / Assumptions of linear regression
  • classifying with / Classifying with linear regression
• linear regression models
  • assessing / Assessing linear regression models
  • residual analysis / Residual analysis
  • tests, used for / Significance tests for linear regression
  • performance metrics / Performance metrics for linear regression
  • comparing / Comparing different regression models
  • outliers / Outliers
• link function
  • about / Generalized linear models
• local kernel
  • about / Kernels and support vector machines
• log-odds
  • about / Generalized linear models
• logistic function
  • form / Introduction to logistic regression
• logistic neuron / The logistic neuron
• logistic regression
  • about / Introduction to logistic regression
  • generalized linear models (GLMs) / Generalized linear models
  • coefficients, interpreting / Interpreting coefficients in logistic regression
  • assumptions / Assumptions of logistic regression
  • maximum likelihood estimation / Maximum likelihood estimation
• logistic regression models
  • assessing / Assessing logistic regression models
  • model deviance / Model deviance
  • test set performance / Test set performance
• logit function
  • about / Generalized linear models
• lynx trappings
  • predicting / Predicting lynx trappings

M

• M5
  • about / Regression model trees
• magic
  • about / Predicting atmospheric gamma ray radiation
• MAGIC Gamma Telescope data set
  • URL / Predicting atmospheric gamma ray radiation
  • attributes / Predicting atmospheric gamma ray radiation
• margin
  • about / Maximal margin classification
  / Margins and out-of-bag observations
• Markov Chain Monte Carlo (MCMC)
  • about / Fitting an LDA model
• matrix, recommendation systems
  • rating / Rating matrix
  • user similarity, measuring / Measuring user similarity
• Matrix Market format
  • about / Modeling the topics of online news stories
• maximal margin classification
  • about / Maximal margin classification
• maximal margin hyperplane
  • about / Maximal margin classification
• maximum likelihood estimation / Maximum likelihood estimation
• McCulloch-Pitts model of a neuron
  • about / The artificial neuron
• mean
  • about / Estimating the regression coefficients
• mean average error (MAE) / Evaluating individual predictions
• mean function
  • about / Generalized linear models, Time series summary functions
• mean squared error (MSE) / Evaluating individual predictions
• Mean Square Error (MSE)
  • about / Assessing regression models
• mean square error (MSE)
  • about / Estimating the regression coefficients
• median
  • about / Residual analysis
• Missing At Random (MAR)
  • about / Missing data
• Missing Completely At Random (MCAR)
  • about / Missing data
• missing data / Missing data
• Missing Not At Random (MNAR)
  • about / Missing data
• missing values
  • handling / Missing data
• mixed selection
  • about / Feature selection
• MLP network
  • about / Multilayer perceptron networks
  • characteristic / Multilayer perceptron networks
  • advantages / Multilayer perceptron networks
  • training / Training multilayer perceptron networks
  • evaluating, for regression / Evaluating multilayer perceptrons for regression
• model
  • task requirements / Picking a model
  • selecting / Repeating with different models and final model selection
• model, deploying
  • guidelines / Deploying the model
• model deviance / Model deviance
• model parameters
  • tuning, in CART trees / Tuning model parameters in CART trees
  • variable importance, in tree models / Variable importance in tree models
  • regression model trees / Regression model trees in action
• models
  • about / Models
  • data, defining / Learning from data
  • components / The core components of a model
  • k-nearest neighbors / Our first model: k-nearest neighbors
• model types
  • about / Types of models
  • supervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
  • unsupervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
  • semi-supervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
  • reinforcement learning model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
  • parametric model / Parametric and nonparametric models
  • nonparametric model / Parametric and nonparametric models
  • regression model / Regression and classification models
  • classification model / Regression and classification models
  • real-time machine learning model / Real-time and batch machine learning models
  • batch machine learning model / Real-time and batch machine learning models
• moving average (MA)
  • about / Moving average models
• moving average models / Moving average models
• multi-class classification, with support vector machines
  • defining / Multiclass classification with support vector machines
• multinom()function
  • about / Predicting glass type
• multinomial logistic regression
  • about / Multinomial logistic regression
  • glass type, predicting / Predicting glass type
• multiple linear regression
  • about / Introduction to linear regression, Multiple linear regression
  • CPU performance, predicting / Predicting CPU performance
  • price of used cars, predicting / Predicting the price of used cars

N

• Naïve Bayes Classifier
  • about / The Naïve Bayes classifier
  • movie reviews, predicting / Predicting the sentiment of movie reviews
• nodes
  • about / The intuition for tree models, A little graph theory
• non-stationary time series models
  • about / Non-stationary time series models
  • ARIMA models / Autoregressive integrated moving average models
  • ARCH models / Autoregressive conditional heteroscedasticity models
  • GARCH models / Generalized autoregressive heteroscedasticity models
• nonparametric model / Parametric and nonparametric models
• nucleotides
  • about / Predicting promoter gene sequences
• nucleus
  • about / The biological neuron
• null deviance
  • about / Model deviance
• null model
  • about / Significance tests for linear regression

O

• odds ratio
  • about / Generalized linear models
• options, feature scaling
  • about / Feature transformations
  • Z-score normalization / Feature transformations
  • unit interval / Feature transformations
  • Box-Cox transformation / Feature transformations
• ordered factor
  • about / Predicting glass type
• ordinal logistic regression
  • about / Predicting glass type, Ordinal logistic regression
  • wine quality, predicting / Predicting wine quality
• out-of-bag (OOB)
  • about / Margins and out-of-bag observations
• out-of-bag observations / Margins and out-of-bag observations
• outliers / Outliers, Outliers
• Overfitting
  • about / Training and assessing the model
  • limitations / Random forests

P

• p-value
  • about / Significance tests for linear regression
• parametric model / Parametric and nonparametric models
• partial autocorrelation function (PACF)
  • about / Autoregressive models
• Perception Action Cycles (PACs)
  • about / Predicting complex skill learning
• perceptron algorithm
  • about / The perceptron algorithm, Linear separation
• performance metrics
  • about / Performance metrics
  • regression models, assessing / Assessing regression models
  • classification models, assessing / Assessing classification models
• performance metrics, for linear regression / Performance metrics for linear regression
• pocket perceptron algorithm
  • about / The perceptron algorithm
  • input / The perceptron algorithm
  • output / The perceptron algorithm
  • method / The perceptron algorithm
• polynomial kernel
  • about / Kernels and support vector machines
• Porter Stemmer
  • about / Predicting the sentiment of movie reviews
  • URL / Predicting the sentiment of movie reviews
• post-pruning
  • about / Tree pruning
• Precision
  • about / Assessing binary classification models
• predictive modeling
  • process / The process of predictive modeling
  • objective, defining / Defining the model's objective
  • data, collecting / Collecting the data
  • model, selecting / Picking a model, Repeating with different models and final model selection
  • data, pre-processing / Preprocessing the data
  • feature engineering / Feature engineering and dimensionality reduction
  • dimensionality reduction / Feature engineering and dimensionality reduction
  • model, training / Training and assessing the model
  • model, assessing / Training and assessing the model
  • model, deploying / Deploying the model
• price, of used cars
  • predicting / Predicting the price of used cars
• Principal Component Analysis (PCA)
  • about / Removing problematic features, Feature engineering and dimensionality reduction
• Principles and Practice of Knowledge Discovery in Databases
  • about / Modeling the topics of online news stories
• probabilistic graphical models
  • about / A little graph theory
• probability
  • about / Learning from data
• promoter gene sequences
  • predicting / Predicting promoter gene sequences
• proportional odds
  • about / Ordinal logistic regression
• pruning
  • about / Tree pruning

Q

• Q-Q plots
  • about / Residual analysis
• QSAR biodegradation
  • URL / Predicting chemical biodegration
• Quantile-Quantile plot (Q-Q plot)
  • about / Residual analysis

R

• R
  • regularization, implementing / Implementing regularization in R
• radial basis function kernel
  • about / Kernels and support vector machines
• radial kernel
  • about / Kernels and support vector machines
• random forest
  • about / Random forests
  • variables, defining / The importance of variables in random forests
• random walk
  • about / Random walk
  • fitting / Fitting a random walk
• real-time machine learning model / Real-time and batch machine learning models
• real-time strategy (RTS)
  • about / Predicting complex skill learning
• Recall
  • about / Assessing binary classification models
• recommendation systems
  • Big Data, handling, in R / R and Big Data
  • building, for movies and jokes / Predicting recommendations for movies and jokes
  • other approaches / Other approaches to recommendation systems
• recursive partitioning
  • about / CART regression trees
• regression coefficients
  • about / Introduction to linear regression
  • estimating / Estimating the regression coefficients
• regression model / Regression and classification models
• regression models
  • assessing / Assessing regression models
• regression model trees
  • about / Regression model trees
  • drawbacks / Regression model trees
  / Regression model trees in action
• regularization
  • about / Regularization
  • ridge regression / Ridge regression
  • least absolute shrinkage / Least absolute shrinkage and selection operator (lasso)
  • selection operator (lasso) / Least absolute shrinkage and selection operator (lasso)
  • implementing, in R / Implementing regularization in R
  • with lasso / Regularization with the lasso
• reinforcement learning model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
• residual analysis / Residual analysis
• residual sum of squares (RSS)
  • about / Performance metrics for linear regression, Model deviance
• ridge regression / Ridge regression
• ROC Area Under the Curve (ROC AUC) / Receiver operating characteristic curves
• ROC curve / Receiver operating characteristic curves
• root mean square error (RMSE) / Evaluating individual predictions
• Root Mean Square Error (RMSE)
  • about / Assessing regression models
• root node
  • about / The intuition for tree models

S

• selection operator (lasso) / Least absolute shrinkage and selection operator (lasso)
• semi-supervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
• sensitivity
  • about / Classification metrics
• sentiment analysis
  • URL / Predicting the sentiment of movie reviews
• Sentiment node
  • URL / Predicting the sentiment of movie reviews
• simple linear regression model
  • about / Introduction to linear regression, Simple linear regression
  • advantages / Simple linear regression
  • regression coefficients, estimating / Estimating the regression coefficients
• singular value decomposition (SVD)
  • about / Singular value decomposition
• Singular Value Decomposition (SVD)
  • about / Removing problematic features
• SkillCraft
  • URL / Predicting complex skill learning
• SkillCraft1 Master Table data set
  • URL / Predicting complex skill learning
• slack variables
  • about / Support vector classification
• softmax function
  • about / Multinomial logistic regression
• spectral methods
  • about / Other time series models
• splines
  • about / Parametric and nonparametric models
• stationarity
  • about / Stationarity
• stationary time series models
  • about / Stationary time series models
  • moving average models / Moving average models
  • autoregressive models (AR) / Autoregressive models
  • ARMA model / Autoregressive moving average models
• Statlog (Heart) data set
  • working with / Predicting heart disease
• step function
  • about / The artificial neuron
• stepwise regression
  • about / Feature selection
• stochastic gradient boosting
  • about / Predicting complex skill learning with boosting
• stochastic gradient descent
  • defining / Stochastic gradient descent
  • about / Stochastic gradient descent
  • gradient descent / Gradient descent and local minima
  • local minima / Gradient descent and local minima
  • perceptron algorithm / The perceptron algorithm, Linear separation
  • logistic neuron / The logistic neuron
• stochastic model
  • about / Learning from data
  • example / Learning from data
• stochastic process
  • about / Fundamental concepts of time series
• stump
  • about / Boosting
• Stuttgart Neural Network Simulator (SNNS) / Predicting handwritten digits
• sum of squared error (SSE) / Predicting the energy efficiency of buildings
  • about / CART regression trees
• Sum of Squared Error (SSE)
  • about / Assessing regression models
• sum of squared errors (SSE)
  • about / Performance metrics for linear regression
• supervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
• support vector classification
  • about / Support vector classification
  • inner products / Inner products
• support vector machines
  • about / Kernels and support vector machines
• Symmetric Dirichlet Distribution
  • about / The Dirichlet distribution
• synaptic neurotransmitters
  • about / The biological neuron
• synthetic 2D data
  • class membership, predicting on / Predicting class membership on synthetic 2D data

T

• tests, for linear regression
  • about / Significance tests for linear regression
• test set
  • about / Training and assessing the model
• time-domain methods
  • about / Other time series models
• time series
  • defining / Fundamental concepts of time series
  • summary functions / Time series summary functions
  • examples / Some fundamental time series
  • white noise / White noise
  • random walk / Random walk
• time series models
  • defining / Other time series models
• topic modeling
  • about / An overview of topic modeling
• topics, of online news stories
  • modeling / Modeling the topics of online news stories
  • model stability / Model stability
  • number of topics, finding / Finding the number of topics
  • topic distribution / Topic distributions
  • word distributions / Word distributions
  • LDA extensions / LDA extensions
• total sum of squares (TSS)
  • about / Performance metrics for linear regression
  • formula / Performance metrics for linear regression
• training set
  • about / Training and assessing the model
• tree models
  • defining / The intuition for tree models
• tree pruning / Tree pruning
• trend
  • about / Other time series models
• true negatives
  • about / Assessing binary classification models
• true positives
  • about / Assessing binary classification models
• true sum of squares (TSS)
  • about / Model deviance
• Type I error
  • about / Assessing binary classification models
• Type II error
  • about / Assessing binary classification models

U

• UCI Machine Learning Repository
  • URL / Predicting CPU performance, Predicting glass type
• UCI Machine Learning repository
  • URL / Predicting promoter gene sequences
• UCI Machine Repository
  • URL / Predicting heart disease
• unit interval / Feature transformations
• unit root tests
  • about / Autoregressive integrated moving average models
• unsupervised model / Supervised, unsupervised, semi-supervised, and reinforcement learning models
• user-based collaborative filtering
  • about / User-based collaborative filtering

V

• Variational Expectation Maximization (VEM)
  • about / Fitting an LDA model, Modeling the topics of online news stories
• Viterbi algorithm
  • about / Hidden Markov models

W

• wavelet transform
  • about / Predicting the authenticity of banknotes
• weight
  • about / The biological neuron
• white noise time series
  • about / White noise
  • fitting / Fitting a white noise time series
• wine quality
  • URL / Predicting wine quality
• word cloud
  • about / Word distributions
• word distributions / Word distributions

Z

• Z-score normalization / Feature transformations