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Applied Predictive Modeling

by Max Kuhn, Kjell Johnson
RM44.00 MYR
Applied Predictive Modeling
Applied Predictive Modeling

Hands-on machine learning guidance without math overload

This is a great pick if you want predictive modeling to feel practical rather than abstract. Readers often like how it walks through the full workflow, from preprocessing to tuning, with real examples and plenty of R code that makes the ideas easier to apply. It feels especially useful for analysts, students, or practitioners who want a book they can actually keep returning to while building models.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.

Applied Predictive Modeling

by Max Kuhn, Kjell Johnson
RM44.00 MYR
Condition guide

Book Condition Guide

Books in Like New Condition

This book may contain very minor cosmetic defects, but it is in pristine condition. There should be no yellowing, no foxing, no water damage, and no annotations of any kind on the cover and the pages. For paperbacks, there should not be any crease marks on the spine. This book is good as new — lucky you!

*Books may be missing bundle media (e.g. CD, e-book code), if included.

Books in Very Good Condition

This book has been used, but it is still in a clean condition. There should be no foxing or annotations of any kind on the inner pages, but a sparse amount may be present on the cover, title pages, or outside edges of the book. There should be no water damage of any kind. For paperbacks, there may be light crease marks on the spine.

*Books may be missing bundle media (e.g. CD, e-book code), if included.

Books in Good Condition

We do our best to ensure the quality of our books, but there is no escaping the wear and tear that comes with time. Slight foxing and some annotations may be present on the pages and the cover. There should be no water damage of any kind. For paperbacks, there may be several crease marks on the spine.

*Books may be missing bundle media (e.g. CD, e-book code), if included.

Books in Well Read Condition

This is a well-read book, which means that the previous owner probably really enjoyed it! The cover and pages may include moderate foxing and annotations, but the text is not obscured and still readable. Moderate cosmetic defects and minor water damage may be present on the edges of the book. For paperbacks, there may be multiple crease marks on the spine.

*Books may be missing bundle media (e.g. CD, e-book code), if included.

 

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ISBN: 9781493979363
Authors: Max Kuhn, Kjell Johnson
Publisher: Springer
Date of Publication: 2019-03-16
Format: Paperback
Related Collections: Science
Related Topics: Education, Mathematics, Computers
Goodreads rating: 4.4
(rated by 344 readers)

Description

Applied Predictive Modeling covers the overall predictive modeling process, beginning with data preprocessing, data splitting, and the foundations of model tuning. It provides intuitive explanations of common and modern regression and classification techniques, with an emphasis on solving real data problems. The text illustrates all parts of the modeling process through hands-on, real-life examples, and each chapter contains extensive R code for each step. This multi-purpose resource can be used as an introduction to predictive modeling, a practitioner’s reference handbook, or a text for advanced undergraduate or graduate-level predictive modeling courses. Each chapter includes problem sets to help solidify the covered concepts and uses data available in the book’s R package. This book is intended for a broad audience as both an introduction to predictive models and a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations, while the emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
 

Hands-on machine learning guidance without math overload

This is a great pick if you want predictive modeling to feel practical rather than abstract. Readers often like how it walks through the full workflow, from preprocessing to tuning, with real examples and plenty of R code that makes the ideas easier to apply. It feels especially useful for analysts, students, or practitioners who want a book they can actually keep returning to while building models.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.