https://github.com/tpoisot/interpretablesdmwithjulia

Slides for the "Interpretable SDM with Julia" workshop
https://github.com/tpoisot/interpretablesdmwithjulia

cross-validation explainable-ml interpretable-machine-learning julia machine-learning naive-bayes shapley-values species-distribution-modeling

Last synced: 3 months ago
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Slides for the "Interpretable SDM with Julia" workshop

• Host: GitHub
• URL: https://github.com/tpoisot/interpretablesdmwithjulia
• Owner: tpoisot
• Created: 2023-08-26T18:40:00.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-27T16:58:50.000Z (about 1 year ago)
• Last Synced: 2024-05-02T06:13:37.505Z (8 months ago)
• Topics: cross-validation, explainable-ml, interpretable-machine-learning, julia, machine-learning, naive-bayes, shapley-values, species-distribution-modeling
• Language: Julia
• Homepage: https://tpoisot.github.io/InterpretableSDMWithJulia/
• Size: 97.3 MB
• Stars: 6
• Watchers: 2
• Forks: 2
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
# Let's build an interpretable SDM (with Julia!)

[![Static Badge](https://img.shields.io/badge/License-CC--BY-green)][ccby] [![Static Badge](https://img.shields.io/badge/View_on-github_pages-skyblue)][slides]

💻 ➕ 🌦️ ➕ 🧠 🟰 😁

This repository has the code associated to the "Interpretable SDM from scratch" workshop.

> Give some [feedback]!

[slides]: https://tpoisot.github.io/InterpretableSDMWithJulia/

[ccby]: https://creativecommons.org/licenses/by/4.0/

[feedback]: https://github.com/tpoisot/InterpretableSDMWithJulia/issues/new?labels=feedback

In order to *run* the code, you need

- quarto (v. 1.4 has been tested)

- julia (v. 1.9 has been tested)

and you would benefit from

- VSCode *or*

- jupyter

> **Note that** running the code might take a while, because it will get a few thousand occurrences from GBIF, and download the full CHELSA1 variables (only once) -- for this reason, whenever it makes sense, the code is multi-threaded