eBook - ePub
Hands-On Ensemble Learning with R
A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
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- English
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eBook - ePub
Hands-On Ensemble Learning with R
A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
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About This Book
Explore powerful R packages to create predictive models using ensemble methods
Key Features
- Implement machine learning algorithms to build ensemble-efficient models
- Explore powerful R packages to create predictive models using ensemble methods
- Learn to build ensemble models on large datasets using a practical approach
Book Description
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.
What you will learn
- Carry out an essential review of re-sampling methods, bootstrap, and jackknife
- Explore the key ensemble methods: bagging, random forests, and boosting
- Use multiple algorithms to make strong predictive models
- Enjoy a comprehensive treatment of boosting methods
- Supplement methods with statistical tests, such as ROC
- Walk through data structures in classification, regression, survival, and time series data
- Use the supplied R code to implement ensemble methods
- Learn stacking method to combine heterogeneous machine learning models
Who this book is for
This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.
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Yes, you can access Hands-On Ensemble Learning with R by Prabhanjan Narayanachar Tattar in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.
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Hands-On Ensemble Learning with R
Table of Contents
Hands-On Ensemble Learning with R
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewer
Packt is Searching for Authors Like You
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
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
Logistic regression for hypothyroid classification
Neural networks
Neural network for hypothyroid classification
Naïve Bayes classifier
Naïve Bayes for hypothyroid classification
Decision tree
Decision tree for hypothyroid classification
Support vector machines
SVM for hypothyroid classification
The right model dilemma!
An ensemble purview
Complementary statistical tests
Permutation test
Chi-square and McNemar test
ROC test
Summary
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
Rule of thumb
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
Analyzing waveform data
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
Adaptive boosting
Gradient boosting
Building it from scratch
Squared-error loss function
Using the adabag and gbm packages
Variable importance
Comparing bagging, random forests, and boosting
Summary
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
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
8. Ensemble Diagnostics
Technical requirements
What is ensemble diagnostics?
Ensemble diversity
Numeric prediction
Class 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
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
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
AirPassengers
co2
uspop
gas
Car Sales
austres
WWWusage
Time series visualization
Core concepts and metrics
Essential time series models
Naïve forecasting
Seasonal, trend, and loess fitting
Exponential smoothing state space model
Auto-regressive Integrated Moving Average (ARIMA) models
Auto-regressive neural networks
Messing it all up
Bagging and time series
Ensemble time series models
Summary
12. What's Next?
A. Bibliography
References
R package references
Index
Hands-On Ensemble Learning with R
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On the personal front, I continue to benefit from the support of my family: my daughter, Pranathi; my wife, Chandrika; and my parents, Lakshmi and Narayanachar. The difference in their support from acknowledgement in earlier books is that now I am in Chennai and they support me from Bengaluru. It involves a lot of sacrifice to allow a writer his private time with writing. I also thank my managers, K. Sridharan, Anirban Singha, and Madhu Rao, at Ford Motor Company for their support. Anirban had gone t...
Table of contents
- Hands-On Ensemble Learning with R