When optimizing an ML model there are a variety of strategies to improve generalization from the training data. We can add a complexity penalty to the loss function, and we can evaluate the loss function on validation data.
| Link | Type | Description |
|---|---|---|
| html pdf | Slides | Overfitting and validation |
| [html] Rmd | Notebook | Validation experiments |
To be updated
Slides for regularization video (PDF)
Notebook for validation
Notebook for lasso
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Loftus (2021, Oct. 6). machine learning 4 data science: 6 Regularization and validation. Retrieved from http://ml4ds.com/weeks/06-regularization/
BibTeX citation
@misc{loftus20216,
author = {Loftus, Joshua},
title = {machine learning 4 data science: 6 Regularization and validation},
url = {http://ml4ds.com/weeks/06-regularization/},
year = {2021}
}