Neural networks and ensemble methods like bagging, random forests, and boosting can greatly increase predictive accuracy at the cost of ease of interpretation.
| Link | Type | Description |
|---|---|---|
| html pdf | Slides | Tree-based methods |
| Rmd | Notebook | Basics of tree algorithms |
To be updated
Slides for (tree) ensembles ([PDF])
Slides for deep learning ([PDF])
Notebook for tree splitting
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. 3). machine learning 4 data science: 9 Less interpretable methods. Retrieved from http://ml4ds.com/weeks/09-trees/
BibTeX citation
@misc{loftus20219,
author = {Loftus, Joshua},
title = {machine learning 4 data science: 9 Less interpretable methods},
url = {http://ml4ds.com/weeks/09-trees/},
year = {2021}
}