Link to Content:

Created/Published/Taught by:

Max Kuhn

Kjell Johnson

Content Found Via:

Applied Predictive Modeling

Free? No

Cost Range:

$50.37 - $89.95

Tags: classification / data preprocessing / feature selection / imbalanced data sets / model tuning / predictive modeling / R / regression

Difficulty Rating:

from Amazon:

“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. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performanceâ€•all of which are problems that occur frequently in practice.

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. The data sets and corresponding code are available in the book’s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.

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”

Recommended Prerequisites: Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.

Go to Content: Applied Predictive Modeling

## By davidmeza1 May 17, 2016 - 1:24 pm

This book was my launch pad into predictive modeling with R. It took my R knowledge to the next level. The authors provide clear examples on how to go through the modeling process, from training to testing, fine tuning and prediction. The examples and code provided helped me truly understand the various algorithms and how to implement them in my organization. It stays on my desk, available for me to reference when needed.

Advanced Beginner- familiar with data science / new to this topic / some prerequisitesIntermediate - had relevant prerequisites / was familiar with this topic

(4) A Lot

Detailed Content Survey:Needed to gain or strengthen a specific skill or understand a specific topic

I have used content from this publisher/series before

Yes

All of it

You need to have a good understanding on using R, in particular, understand data structures, functions and data transformation

R, RStudio(suggested), caret package, fastest process you are willing to buy

TIdy Data by Hadley Wickham, vignette on the dplyr package

Some best practices for training and testing your model and model tuning. I also received a decent understanding of various alogorithms.

The exercises and code examples

Did not have one.

Have at least a cursory knowledge of the different model types before you start that section. Use comments throughout your code.You will find this helpful when you return a later and need to understand why you wrote the code a certain way.

I read the first 4 chapters to lay the ground work. Then I moved to models I used regularly or had an interest in using, then I completed the remaining models.

I took one of Max's course at Predictive Analytics World and then spent the next three months, 10 hours a week going over the model. I refer to the book at least monthly.

Data Science Enthusiast and Practitioner

Advanced R, http://adv-r.had.co.nz/ and R for Data Science, http://r4ds.had.co.nz/