Stat Tenacity Books on Statistical Modeling
Stat Tenacity specializes in making statistical modeling understandable for non-statisticians. Need to gain a better grasp of applied predictive modeling? Check out our books on Amazon.
Applied Predictive Modeling
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.”
Feature Engineering and Selection
Gain better tools to extract information from your data. This book equips you to get into your data piece and determine if the data in its current form is the best way to use it. Often, you need to work with the data to discover the patterns within it.
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. Feature Engineering and Selection describes techniques for finding the best representations of predictors for modeling and finding the best subset of predictors for improving model performance.
A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
This book is a companion to Applied Predictive Modeling.
Praise for Feature engineering and selection
“The book is timely and needed. The interest in all things ‘data science’ morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this―as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference.”
“I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more.”