Applied Predictive Modeling
ISBN-13:
9781493979363
ISBN-10:
1493979361
Edition:
Softcover reprint of the original 1st ed. 2013
Author:
Max Kuhn, Kjell Johnson
Publication date:
2019
Publisher:
Springer
Format:
Paperback
613 pages
Category:
Mathematical & Statistical
,
Software
FREE US shipping
on ALL non-marketplace orders
Rent
35 days
Due Jun 05, 2024
35 days
from $41.36
USD
Marketplace
from $46.60
USD
Marketplace offers
Seller
Condition
Note
Seller
Condition
Used - Good
Book details
ISBN-13:
9781493979363
ISBN-10:
1493979361
Edition:
Softcover reprint of the original 1st ed. 2013
Author:
Max Kuhn, Kjell Johnson
Publication date:
2019
Publisher:
Springer
Format:
Paperback
613 pages
Category:
Mathematical & Statistical
,
Software
Summary
Applied Predictive Modeling (ISBN-13: 9781493979363 and ISBN-10: 1493979361), written by authors
Max Kuhn, Kjell Johnson, was published by Springer in 2019.
With an overall rating of 3.7 stars, it's a notable title among other
Mathematical & Statistical
(Software) books. You can easily purchase or rent Applied Predictive Modeling (Paperback) from BooksRun,
along with many other new and used
Mathematical & Statistical
books
and textbooks.
And, if you're looking to sell your copy, our current buyback offer is $24.05.
Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process.
This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package.This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an 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.
We would LOVE it if you could help us and other readers by reviewing the book
Book review
Congratulations! We have received your book review.
{user}
{createdAt}
by {truncated_author}