Digital Resources for Chapter 6: Polynomial Regression - Overfitting & Tuning Explained

Below are digital resources that complement the book Practical Machine Learning with R: Tutorials and Case Studies.

Polynomial Regression Video

Mike X. Cohen provides a YouTube video that explains the basic idea of Polynomial Regression.

The Danger of Overfitting

This video by Cassie Kozyrkov, former Chief Decision Scientist at Google, explains why splitting data into training and testing data is important. She also explains why overfitting is a problem.

Supported Recipe Steps for Preprocessing

Here is a list of all recipe step_() commands that can be piped with |> to a recipe. The linked website will tell you which steps are available for which preprocessing purpose.

Supported Machine Learning Models from tidymodels

Here is a list of all supported tidymodels machine learning models. The linked website will tell you for each model:

the model name
the package name(s) for the set_engine() command
the hyper-parameters that you can tune

A 10-Step Template to Create, Tune, and Assess a Machine Learning Model with tidymodels

The link below will open a blog article by Carsten Lange. The article provides a tidymodels 10-step template for creating, tuning, and assessing machine learning models. The template is explained in detail and a link for downloading the related R script is provided.