Regression with many predictor variables can suffer from a statistical version of the curse of dimensionality. Penalized regression methods like ridge and lasso are useful in such high-dimensional settings.
| Link | Type | Description |
|---|---|---|
| html pdf | Slides | Ridge and lasso regression |
| html Rmd | Notebook | Lasso estimation |
| html Rmd | Notebook | Lasso inference |
To be updated
Slides for regularization video (PDF)
Notebook for validation
Notebook for lasso
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For attribution, please cite this work as
Loftus (2021, Oct. 5). machine learning 4 data science: 7 High-dimensional regression. Retrieved from http://ml4ds.com/weeks/07-highdim/
BibTeX citation
@misc{loftus20217,
author = {Loftus, Joshua},
title = {machine learning 4 data science: 7 High-dimensional regression},
url = {http://ml4ds.com/weeks/07-highdim/},
year = {2021}
}