Index
A
- adabag 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
- 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
- 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
- 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 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