Hands-On Ensemble Learning with R电子书

Hands-On Ensemble Learning with R

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Preface

Who this book is for

What this book covers

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Conventions used

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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 h2o 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

Index

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y