Book Image

Hands-On Ensemble Learning with R

By : Prabhanjan Narayanachar Tattar
Book Image

Hands-On Ensemble Learning with R

By: Prabhanjan Narayanachar Tattar

Overview of this book

Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.

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Table of Contents (17 chapters)
Hands-On Ensemble Learning with R
Hands-On Ensemble Learning with R
Contributors
Contributors
Preface
Preface
Free Chapter
1
Introduction to Ensemble Techniques
Introduction to Ensemble Techniques
Datasets
Statistical/machine learning models
The right model dilemma!
An ensemble purview
Complementary statistical tests
Summary
2
Bootstrapping
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
Bagging
Technical requirements
Classification trees and pruning
Bagging
k-NN classifier
k-NN bagging
Summary
4
Random Forests
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
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
6
Boosting Refinements
Boosting Refinements
Technical requirements
Why does boosting work?
The gbm package
The xgboost package
The h2o package
Summary
7
The General Ensemble Technique
The General Ensemble Technique
Technical requirements
Why does ensembling work?
Ensembling by voting
Ensembling by averaging
Stack ensembling
Summary
8
Ensemble Diagnostics
Ensemble Diagnostics
Technical requirements
What is ensemble diagnostics?
Ensemble diversity
Pairwise measure
Interrating agreement
Summary
9
Ensembling Regression Models
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
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
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
12
What's Next?
What's Next?
Bibliography
Bibliography
References
R package references
Index
Index

Bootstrap and testing hypotheses

We begin the bootstrap hypothesis testing problems with the t-test to compare means and the F-test to compare variances. It is understood that, since we are assuming normal distribution for the two populations under comparison, the distributional properties of the test statistics are well known. To carry out the nonparametric bootstrap for the t-statistic based on the t-test, we first define the function, and then run the bootstrap function boot on the Galton dataset. The Galton dataset is available in the galton data.frame from the RSADBE package. The galton dataset consists of 928 pairs of observations, with the pair consisting of the height of the parent and the height of their child. First, we define the t2 function, load the Galton dataset, and run the boot function as the following unfolds:

> t2 <- function(data,i) {
+   p <- t.test(data[i,1],data[i,2],var.equal=TRUE)$statistic
+   p
+ }
> data(galton)
> gt <- boot(galton,t2,R=100)...
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