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*

- ISLR The rest of Chapter 6.

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} }