Below are digital resources that complement the book Practical Machine Learning with R: Tutorials and Case Studies.
A free DataCamp tutorial about Ridge, Lasso, and ElasticNet regularization that also discusses the trade-off between bias and variance in machine learning.
A video and article by Julia Silge published in her blog TidyTuesday. The post describes how to use tidymodels to analyze data from the TV series “The Office”. A Lasso approach is tuned to regularize the model-parameters for a linear regression.
The first part of two articles by Siddant Rai in MLearning.ai. The author describes requirements for regularization in Part 1 and regularisation techniques in Part 2.
This blog post by Carsten Lange discusses normalization. It shows that although normalization is not needed for linear OLS regression, it is needed when a penalty term is used, including Lasso, Ridge, and Elastic-Net regressions. The blog post article is interactive and provides an R script with an intuitive example.
A short and technical article by Jun Kang in Jun's Blog. The article shows how to regularize a Logistic Regression model using tidymodels and glmnet. The goal is to detect breast cancer from a breast cancer dataset.