Applied Predictive Modeling
Discover How to Make the Most of Predictive Models in Your Work
Most books on predictive modeling focus on the mathematics of models or they’re just software manuals. Applied Predictive Modeling is a go-to book for gaining an in-depth understanding of how to practically use the models. In the book, you’ll discover:
- An intuitive description of models
- Practical aspects of applying them
- Software and data sets so you can reproduce your work
Applied Predictive Modeling is, co-authored by Stat Tenacity founder Kjell Johnson. It covers the overall predictive modeling process. It’s written for a broad audience as an introduction to predictive models and as a guide to applying them. Whether you’re a non-mathematical reader or a practitioner, you’ll extend your expertise in the area of predictive modeling.
Applied Predictive Modeling is the winner of the 2014 Technometrics Ziegel Prize for Outstanding Book.
Praise for Applied Predictive Modeling
“There are hundreds of books that have something worthwhile to say about predictive modeling. However, in my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list ...They come across like coaches who really, really want you to be able to do this stuff.”
“Thanks for writing your fantastic textbook, Applied Predictive Modeling. Right now I’m enjoying working my way through it. As a military intelligence professional, I’m always looking for different ways to examine complex/wicked problems. You and your coauthor make a complex subject comprehensible and enjoyable to digest. I’m especially interested to see where the field of predictive analysis and the social sciences evolve.”
“Applied Predictive Modeling aims to expose many of these techniques in a very readable and self-contained book. This is a very applied and hands-on book. It guides the reader through many examples that serve to illustrate main points, and it raises possible issues and considerations that are oftentimes overlooked or not sufficiently reflected upon. Highly recommended.”