Index

A

• adabag packages
  • using / Using the adabag and gbm packages
• adaptive boosting / Adaptive boosting
• Adaptive boosting algorithm
  • about / Why does boosting work?
  • working / Why does boosting work?
• additive effect / Exponential smoothing state space model
• advantages, extreme gradient boosting implementation
  • parallel computing / The xgboost package
  • regularization / The xgboost package
  • cross-validation / The xgboost package
  • pruning / The xgboost package
  • missing values / The xgboost package
  • saving and reloading / The xgboost package
  • cross platform / The xgboost package
• amyotrophic lateral sclerosis (ALS) / Squared-error loss function
• area under curve (AUC) / Complementary statistical tests
• auto-correlation function (ACF) / Core concepts and metrics
• Auto-regressive Integrated Moving Average (ARIMA) models / Auto-regressive Integrated Moving Average (ARIMA) models

B

• bagging
  • comparing, with random forests / Comparing bagging, random forests, and boosting
  • comparing, with boosting / Comparing bagging, random forests, and boosting
  • for regression data / Bagging and Random Forests
• bagging technique
  • describing / Bagging and time series
• board stiffness dataset / Board Stiffness
• Boostap AGGregatING (bagging) / Bagging
• boot package / The boot package
• Bootstrap
  • about / Bootstrap – a statistical method
  • standard error of correlation coefficient / The standard error of correlation coefficient
  • parametric bootstrap / The parametric bootstrap
  • eigen values / Eigen values
  • rule of thumb / Rule of thumb
• bootstrap hypothesis testing problems / Bootstrap and testing hypotheses

C

• Chi-square Automatic Interaction Detector (CHAID) / Random Forests
• chi-square test / Chi-square and McNemar test
• Classification and Regression Trees (CART) / Random Forests
  • advanatges / Random Forests
  • drawbacks / Random Forests
• classification trees / Classification trees and pruning
• class prediction / Class prediction
• Cohen's statistic / Cohen's statistic
• complementary statistical tests
  • about / Complementary statistical tests
  • permutation test / Permutation test
  • chi-square test / Chi-square and McNemar test
  • McNemar test / Chi-square and McNemar test
  • ROC test / ROC test
• complexity parameter (Cp) / Classification trees and pruning
• contingency table
  • about / Pairwise measure
• correlation coefficient measure / Correlation coefficient measure
• Cox proportional hazards models / Regression models – parametric and Cox proportional hazards models

D

• data
  • pre-processing / Pre-processing the housing data
  • housing / Pre-processing the housing data
• datasets
  • about / Datasets
  • hypothyroid datasets / Hypothyroid
  • waveform datasets / Waveform
  • German Credit / German Credit
  • Iris / Iris
  • Pima Indians Diabetes / Pima Indians Diabetes
  • US Crime / US Crime
  • Overseas Visitors / Overseas visitors
  • Primary Biliary Cirrhosis / Primary Biliary Cirrhosis
  • multishapes / Multishapes
  • board stiffness dataset / Board Stiffness
  • selecting / The right model dilemma!
• decision tree
  • about / Decision tree
  • for hypothyroid classification / Decision tree for hypothyroid classification
• disagreement measure / Disagreement measure
  • for ensemble / Disagreement measure for ensemble
• double-fault measure / Double-fault measure

E

• ensemble
  • need for / An ensemble purview
  • disagreement measure / Disagreement measure for ensemble
• ensemble diagnostics
  • about / What is ensemble diagnostics?
• ensemble diversity
  • about / Ensemble diversity
  • numeric prediction / Numeric prediction
  • class prediction / Class prediction
• ensemble survival models / Ensemble survival models
• ensembling
  • working / Why does ensembling work?
  • by voting / Ensembling by voting
  • by averaging / Ensembling by averaging
• ensembling, by averaging
  • about / Ensembling by averaging
  • simple averaging / Simple averaging
  • weight averaging / Weight averaging
• ensembling, by voting
  • majority voting / Majority voting
  • weighted voting / Weighted voting
• entropy measure / Entropy measure
• Exponential Distribution / Core concepts of survival analysis
• exponential models
  • reference / Exponential smoothing state space model
• exponential smoothing state space model / Exponential smoothing state space model

F

• functional-delta theorem / Nonparametric inference

G

• Gamma Distribution / Core concepts of survival analysis
• gbm package
  • about / The gbm package
  • reference / The gbm package
  • boosting, for count data / Boosting for count data
  • boosting, for survival data / Boosting for survival data
• gbm packages
  • using / Using the adabag and gbm packages
• general boosting algorithm / The general boosting algorithm
• German Credit
  • about / German Credit
  • reference / German Credit
• German credit dataset / Classification trees and pruning
• gradient boosting algorithm
  • about / Gradient boosting
  • building, from scratch / Building it from scratch
  • squared-error loss function / Squared-error loss function

H

• h2o package
  • about / The h2o package
  • reference / The h2o package
• hazards regression model / Regression models – parametric and Cox proportional hazards models
• hypothyroid dataset
  • about / Hypothyroid
  • reference / Hypothyroid

I

• interrater agreement
  • about / Interrating agreement
  • entropy measure / Entropy measure
  • Kohavi-Wolpert measure / Kohavi-Wolpert measure
  • measurement / Measurement of interrater agreement
• Iris dataset
  • about / Iris
• iterative reweighted least squares (IRLS) algorithm / Logistic regression model

J

• jackknife technique
  • about / The jackknife technique
  • for mean and variance / The jackknife method for mean and variance
  • pseudovalues method for survival data / Pseudovalues method for survival data

K

• k-NN bagging / k-NN bagging
• k-NN classifier / k-NN classifier, Analyzing waveform data
• Kaplan-Meier estimator / Nonparametric inference
• Kohavi-Wolpert measure / Kohavi-Wolpert measure

L

• linear regression model / Linear regression model
• logistic regression model
  • about / Logistic regression model
  • for hypothyroid classification / Logistic regression for hypothyroid classification

M

• McNemar test / Chi-square and McNemar test
• memoryless property / Core concepts of survival analysis
• metrics / Core concepts and metrics
• missForest function
  • reference / Missing data imputation
• missing data
  • handling, random forests used / Missing data imputation
• modeling dilemma / The right model dilemma!
• multishapes dataset / Multishapes
• multivariate statistics / Visualization and variable reduction

N

• Naïve Bayes classifier
  • about / Naïve Bayes classifier
  • for hypothyroid classification / Naïve Bayes for hypothyroid classification
• Nelson-Aalen estimator / Nonparametric inference
• neural networks
  • about / Neural networks
  • for hypothyroid classification / Neural network for hypothyroid classification
  / Neural networks
• nonparametric inference / Nonparametric inference
• number prediction / Numeric prediction

O

• Overseas Visitors dataset
  • about / Overseas visitors
  • reference / Overseas visitors

P

• pairwise measure
  • about / Pairwise measure
  • disagreement measure / Disagreement measure
  • Yule's coefficient / Yule's or Q-statistic
  • Q-statistic / Yule's or Q-statistic
  • correlation coefficient measure / Correlation coefficient measure
  • Cohen's statistic / Cohen's statistic
  • double-fault measure / Double-fault measure
• partial auto-correlation function (PACF)
  • about / Core concepts and metrics
  • reference / Core concepts and metrics
• partial likelihood function / Regression models – parametric and Cox proportional hazards models
• permutation test / Permutation test
• Pima Indians Diabetes dataset / Pima Indians Diabetes
• Primary Biliary Cirrhosis dataset
  • about / Primary Biliary Cirrhosis
• Principal Component Analysis (PCA) / Visualization and variable reduction
• proximity plots
  • using / Proximity plots
• pruning / Classification trees and pruning

Q

• Q-statistic / Yule's or Q-statistic

R

• random forest
  • used, for clustering / Clustering with Random Forest
• Random Forest algorithm
  • about / Random Forests
• random forest nuances / Random Forest nuances
• random forests
  • comparing, with bagging / Comparisons with bagging
  • used, for handling missing data / Missing data imputation
  • used, for clustering / Clustering with Random Forest
• Random Forests
  • for regression data / Bagging and Random Forests
• raters
  • about / Pairwise measure
• regression models
  • bootstrapping / Bootstrapping regression models
  • about / Regression models, Regression models – parametric and Cox proportional hazards models
  • linear regression model / Linear regression model
  • neural networks / Neural networks
  • regression tree / Regression tree
  • prediction / Prediction for regression models
  • boosting / Boosting regression models
  • stacking methods / Stacking methods for regression models
  • Cox proportional hazards models / Regression models – parametric and Cox proportional hazards models
  • hazards regression model / Regression models – parametric and Cox proportional hazards models
• regression tree / Regression tree
• residual bootstrapping method / Bootstrapping regression models
• ROC test / ROC test

S

• split function / Bagging and Random Forests
• stack ensembling / Stack ensembling
• stacking methods
  • for regression models / Stacking methods for regression models
• statistical/machine learning models
  • about / Statistical/machine learning models
  • logistic regression model / Logistic regression model
  • neural networks / Neural networks
  • Naïve Bayes classifier / Naïve Bayes classifier
  • decision tree / Decision tree
  • support vector machines / Support vector machines
• support vector machines
  • about / Support vector machines
  • for hypothyroid classification / SVM for hypothyroid classification
• survival analysis
  • about / Core concepts of survival analysis
• Survival Models
  • bootstrapping / Bootstrapping survival models*
• survival tree
  • about / Survival tree

T

• time series datasets
  • about / Time series datasets
  • AirPassengers dataset / AirPassengers
  • co2 time series data / co2
  • uspop / uspop
  • gas time series data / gas
  • car sales data / Car Sales
  • austres time series dataset / austres
  • WWWusage time series dataset / WWWusage
• time series models
  • bootstrapping / Bootstrapping time series models*
  • about / Essential time series models
  • Naïve forecasting / Naïve forecasting
  • seasonal / Seasonal, trend, and loess fitting
  • trend / Seasonal, trend, and loess fitting
  • loess fitting / Seasonal, trend, and loess fitting
  • exponential smoothing state space model / Exponential smoothing state space model
  • Auto-regressive Integrated Moving Average (ARIMA) models / Auto-regressive Integrated Moving Average (ARIMA) models
  • auto-regressive neural networks / Auto-regressive neural networks
  • linear model (LM) / Messing it all up
  • messing up / Messing it all up
  • ensembling / Ensemble time series models
• time series visualization / Time series visualization

U

• US Crime dataset / US Crime

V

• variable clustering / Variable clustering
• variable importance
  • for decision trees and random forests / Variable importance
  / Variable importance
• variable reduction
  • about / Visualization and variable reduction
  • techniques / Visualization and variable reduction
• visualization / Visualization and variable reduction

W

• waveform datasets / Waveform
• Weibull Distribution / Core concepts of survival analysis

X

• xgboost package
  • about / The xgboost package
  • reference / The xgboost package
• xgboost technique
  • reference / The xgboost package

Y

• Yule's coefficient / Yule's or Q-statistic