index.qmd

---
format:
  html:
    echo: false
---

```{python}
from platform import python_version
import subprocess

quarto_version = (
  subprocess.run('quarto --version', capture_output=True)
  .stdout.decode().strip()
)
```

# Preface  {.unnumbered}

This is a computing supplement to the main website that uses python, and in particular scikit-learn for modeling. The structure is similar to the website, but the content here shows how to use this software for each topic.

<!-- We also want these materials to be reusable and open. The sources are in the source [GitHub repository](https://github.com/aml4td/computing-tidymodels) with a Creative Commons license attached (see below). -->

<!-- To cite this work, we suggest:  -->

## License {.unnumbered}

 <p xmlns:cc="http://creativecommons.org/ns#" >As this computing supplement will largely be adapting the book, we adopt here the same license, <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">CC BY-NC-SA 4.0<img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1"></a></p> 

## Intended Audience {.unnumbered}

Readers should have used python before, but do not have to be experts. 
If you are new to python, we suggest taking a look at the [_Python Data Science Handbook_](https://jakevdp.github.io/PythonDataScienceHandbook/). 

You do not have to be a modeling expert either. We hope that you have used a linear or logistic regression before and understand basic statistical concepts such as correlation, variability, probabilities, etc. 

## How can I ask questions? 

If you have questions about the content, it is probably best to ask on a public forum, like [Stack Overflow](https://stackoverflow.com) for programmatic questions, or the [data science](https://datascience.stackexchange.com) or [statistics](https://stats.stackexchange.com/) Stack Exchange sites. You'll most likely get a faster answer there if you take the time to ask the questions in the best way possible.

If you want a direct answer from us, you should follow what Max calls [_Yihui's Rule_](https://yihui.org/en/2017/08/so-gh-email/): add an issue to GitHub (labeled as "Discussion") first. It may take some time for us to get back to you. 

<!-- If you think there is a bug, please [file an issue](https://github.com//aml4td/computing-tidymodels/issues).  -->

## Can I contribute?  {.unnumbered}

There is a [contributing page](chapters/contributing.html) with details on how to get up and running to compile the materials and suggestions on how to help. 

If you just want to fix a typo, you can make a pull request to alter the appropriate `.qmd` file. 

Please feel free to improve the quality of this content by submitting **pull requests**. 
<!-- A merged PR will make you appear in the contributor list.  -->

## Computing Notes {.unnumbered}

[Quarto](https://quarto.org/) version `{python} quarto_version` was used to compile and render the materials.  
<!-- (View this page's source html, and you should find the version of Quarto used.) -->

Python version `{python} python_version()` was used for computations. 
For the list of python packages used and their versions, see the Pipfile in the source repository.