Hands-On Ensemble Learning with R
Prabhanjan Narayanachar Tattar更新时间：2021-07-23 19:11:24
最新章节：R package references封面
Hands-On Ensemble Learning with R
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PacktPub.com
Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Chapter 1. Introduction to Ensemble Techniques
Datasets
Hypothyroid
Waveform
German Credit
Iris
Pima Indians Diabetes
US Crime
Overseas visitors
Primary Biliary Cirrhosis
Multishapes
Board Stiffness
Statistical/machine learning models
Logistic regression model
Neural networks
Naïve Bayes classifier
Decision tree
Support vector machines
The right model dilemma!
An ensemble purview
Complementary statistical tests
Permutation test
Chi-square and McNemar test
ROC test
Summary
Chapter 2. Bootstrapping
Technical requirements
The jackknife technique
The jackknife method for mean and variance
Pseudovalues method for survival data
Bootstrap – a statistical method
The standard error of correlation coefficient
The parametric bootstrap
Eigen values
The boot package
Bootstrap and testing hypotheses
Bootstrapping regression models
Bootstrapping survival models*
Bootstrapping time series models*
Summary
Chapter 3. Bagging
Technical requirements
Classification trees and pruning
Bagging
k-NN classifier
Analyzing waveform data
k-NN bagging
Summary
Chapter 4. Random Forests
Technical requirements
Random Forests
Variable importance
Proximity plots
Random Forest nuances
Comparisons with bagging
Missing data imputation
Clustering with Random Forest
Summary
Chapter 5. The Bare Bones Boosting Algorithms
Technical requirements
The general boosting algorithm
Adaptive boosting
Gradient boosting
Using the adabag and gbm packages
Variable importance
Comparing bagging random forests and boosting
Summary
Chapter 6. Boosting Refinements
Technical requirements
Why does boosting work?
The gbm package
Boosting for count data
Boosting for survival data
The xgboost package
The h1o package
Summary
Chapter 7. The General Ensemble Technique
Technical requirements
Why does ensembling work?
Ensembling by voting
Majority voting
Weighted voting
Ensembling by averaging
Simple averaging
Weight averaging
Stack ensembling
Summary
Chapter 8. Ensemble Diagnostics
Technical requirements
What is ensemble diagnostics?
Ensemble diversity
Numeric prediction
Pairwise measure
Disagreement measure
Yule's or Q-statistic
Correlation coefficient measure
Cohen's statistic
Double-fault measure
Interrating agreement
Entropy measure
Kohavi-Wolpert measure
Disagreement measure for ensemble
Measurement of interrater agreement
Summary
Chapter 9. Ensembling Regression Models
Technical requirements
Pre-processing the housing data
Visualization and variable reduction
Variable clustering
Regression models
Linear regression model
Neural networks
Regression tree
Prediction for regression models
Bagging and Random Forests
Boosting regression models
Stacking methods for regression models
Summary
Chapter 10. Ensembling Survival Models
Core concepts of survival analysis
Nonparametric inference
Regression models – parametric and Cox proportional hazards models
Survival tree
Ensemble survival models
Summary
Chapter 11. Ensembling Time Series Models
Technical requirements
Time series datasets
Time series visualization
Core concepts and metrics
Essential time series models
Bagging and time series
Ensemble time series models
Summary
Chapter 12. What's Next?
Appendix A. Bibliography
References
R package references
更新时间：2021-07-23 19:11:24