We are excited to share a free extract of Zumel, Mount, *Practical Data Science with R, 2nd Edition*, Manning 2019: Evaluating a Classification Model with a Spam Filter.

This section reflects an important design decision in the book: teach model evaluation first, and as a step separate from model construction.

It is funny, but it takes some effort to teach in this way. New data scientists want to dive into the details of model construction first, and statisticians are used to getting model diagnostics as a side-effect of model fitting. However, to compare different modeling approaches one really needs good model evaluation that is independent of the model construction techniques.

This teaching style has worked very well for us both in R and in Python (it is considered one of the merits of our LinkedIn AI Academy course design):

(Note: Nina Zumel, leads on the course design, which is the heavy lifting, John Mount just got tasked to be the one delivering it.)

Zumel, Mount, *Practical Data Science with R, 2nd Edition* is coming out in print *very* soon. Here is a discount code to help you get a good deal on the book:

Take 37% off Practical Data Science with R, Second Edition by entering

fcczumel3into the discount code box at checkout at manning.com.

Categories: Administrativia data science Exciting Techniques Opinion Practical Data Science Pragmatic Data Science Pragmatic Machine Learning Statistics

### jmount

Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.