Hands-On Ensemble Learning with R

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.