Jump to content

Dear members, finally, we decided to refresh our theme. Decision was brought based on multiple factors, primarily because of technical needs as old one is not compatible with a new platform version, but also because you all asked for a darker theme.
Here you go!

Please head here if you want to vote https://www.elite7hackers.net/topic/411861-the-new-theme/


This site uses cookies! Learn More

This site uses cookies!

For providing our services, we do use cookies.
But get used, this is what most of modern web do!
However we have to warn you since we are obligated to so due to EU laws.

By continuing to use this site, you agree to allow us to store cookies on your computer. :)
And no, we will not eat your computer nor you will be able to eat those cookies :P


This topic is now archived and is closed to further replies.


Hands-On Ensemble Learning with R

Recommended Posts


Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques by Prabhanjan Narayanachar Tattar
English | 27 July 2018 | ISBN: 1788624149 | 376 Pages | EPUB | 7.93 MB

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
Introduction to Ensemble Techniques
Random Forests
The Bare Bones Boosting Algorithms
Boosting Refinements
The General Ensemble Technique
Ensemble Diagnostics
Ensembling Regression Models
Ensembling Survival Models
Ensembling Time Series Models
What's Next?



Share this post

Link to post
Share on other sites

Elite7Hackers Netwok

Hack the imagination!

Support and inquiries

Open support ticket here or email us at [email protected]


Highlighted/recommended lights


Important Information

By using this site, you agree to our Privacy Policy and Terms of Use.