Hands-On Ensemble Learning with R

Tattar, Prabhanjan

ISBN 10: 1788624149 / ISBN 13: 9781788624145

Editore: Packt Publishing, 2018

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This book introduces you to the concept of ensemble learning and demonstrates how different machine learning algorithms can be combined to build efficient machine learning models. Use R to implement the popular trilogy of ensemble techniques, i.e. bagging, . Codice articolo 448328646

Dati bibliografici

Titolo: Hands-On Ensemble Learning with R

Casa editrice: Packt Publishing

Data di pubblicazione: 2018

Legatura: Brossura

Condizione: New

Descrizione articolo

Riassunto:

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.

Table of Contents

1. Introduction to Ensemble Techniques
2. Bootstrapping
3. Bagging
4. Random Forests
5. The Bare Bones Boosting Algorithms
6. Boosting Refinements
7. The General Ensemble Technique
8. Ensemble Diagnostics
9. Ensembling Regression Models
10. Ensembling Survival Models
11. Ensembling Time Series Models
12. What's Next?