Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Tattar

£36.99

3 (1 Ratings)

Paperback Jul 2018 376 pages 1st Edition

Tattar

£36.99

3 (1 Ratings)

Paperback Jul 2018 376 pages 1st Edition

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Key benefits

•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

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.

Who is this book 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.

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

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Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

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UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

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American Samoa
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Brunei Darussalam
Central African Republic
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Guinea-bissau
Iran
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Russian Federation
Syrian Arab Republic
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Grady Heller Aug 30, 2023

The book itself was poorly printed and the content is basically just code put together without sufficient explanation and is more of a basic cook book.Dislikes- print was straight up BAD with print errors and some illegible charts, weird grammar here and there, and a number of things are just not explained by the author - if I didn't already know machine learning to some extent I'd be lost and it can also just be very hard to follow the author and that's a rough combination.Likes - does provide some intro info on ensembles, lots of basic coding examples, includes sections on survival and time series applications to help round out the content, and I do find the book to be practical if you just want to get a start with ensemble techniques after learning basic machine learning techniques.

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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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