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You're reading from Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788624145

Length 376 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Prabhanjan Narayanachar Tattar

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Table of Contents (15) Chapters

Preface

Who this book is for
What this book covers
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1. Introduction to Ensemble Techniques FREE CHAPTER

Datasets
Statistical/machine learning models
The right model dilemma!
An ensemble purview
Complementary statistical tests
Summary

2. Bootstrapping

Technical requirements
The jackknife technique
Bootstrap – a statistical method
The boot package
Bootstrap and testing hypotheses
Bootstrapping regression models
Bootstrapping survival models*
Bootstrapping time series models*
Summary

3. Bagging

Technical requirements
Classification trees and pruning
Bagging
k-NN classifier
k-NN bagging
Summary

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

5. The Bare Bones Boosting Algorithms

Technical requirements
The general boosting algorithm
Summary

6. Boosting Refinements

Technical requirements
Why does boosting work?
The gbm package
The xgboost package
The h2o package
Summary

7. The General Ensemble Technique

Technical requirements
Why does ensembling work?
Ensembling by voting
Ensembling by averaging
Stack ensembling
Summary

8. Ensemble Diagnostics

Technical requirements
What is ensemble diagnostics?
Ensemble diversity
Interrating agreement
Summary

9. Ensembling Regression Models

Technical requirements
Pre-processing the housing data
Visualization and variable reduction
Regression models
Bagging and Random Forests
Boosting regression models
Stacking methods for regression models
Summary

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

11. Ensembling Time Series Models

Technical requirements
Time series datasets
Summary

12. What's Next?

A. Bibliography

R package references

Index

Technical requirements

We will be using the following libraries in this chapter:

kernlab
randomForest
randomForestExplainer
rpart

The rest of the chapter is locked

You're reading from Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Table of Contents (15) Chapters

Technical requirements

Authors (1)

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You're reading from Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Table of Contents (15) Chapters

Technical requirements

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Authors (1)

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