Build better training data

Overview

• Identify the importance of preprocessing data sets
• Introduce the recipes package for preprocessing data
• Utilize usemodels to automatically construct code templates for common model types
• Construct workflows for machine learning

Before class

• Read Preprocess your data

This is not a math/stats class. In class we will briefly summarize how these methods work and spend the bulk of our time on estimating and interpreting these models. That said, you should have some understanding of the mathematical underpinnings of statistical learning methods prior to implementing them yourselves. See below for some recommended readings:

• Chapter 5 in An Introduction to Statistical Learning
• Chapters 2-3 in Hands-On Machine Learning with R
• Feature Engineering and Selection: A Practical Approach for Predictive Models

Class materials

usethis::use_course("cis-ds/machine-learning")

Additional readings

• caret
• tidymodels
• Tidy Modeling with R - a book-length introduction to tidy modeling in R
• ISLR tidymodels Labs - complement to the 2nd edition of Introduction to Statistical Learning with translations of the labs into using the tidymodels set of packages.

What you need to do after class