https://github.com/pbiecek/xai_stories_2
XAI Stories 2.0. eXplainable Artificial Intelligence for Retail Analytics - case studies
https://github.com/pbiecek/xai_stories_2
analytics iml retail xai
Last synced: 7 months ago
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XAI Stories 2.0. eXplainable Artificial Intelligence for Retail Analytics - case studies
- Host: GitHub
- URL: https://github.com/pbiecek/xai_stories_2
- Owner: pbiecek
- Created: 2021-05-26T22:02:01.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-06-22T12:39:46.000Z (over 4 years ago)
- Last Synced: 2025-01-30T13:15:19.410Z (9 months ago)
- Topics: analytics, iml, retail, xai
- Language: TeX
- Homepage: https://pbiecek.github.io/xai_stories_2/
- Size: 7.37 MB
- Stars: 6
- Watchers: 3
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# XAI Stories 2.0. eXplainable Artificial Intelligence for Retail Analytics
ebook: https://pbiecek.github.io/xai_stories_2/
In 2020, as part of the Interpretable Machine Learning course, students created XAI Stories, an ebook that collects the experiences of the subjects covered in the form of a series of chapters on different applications of XAI techniques.
This was a great idea. Each team developed an interesting solution and then described it in a clear and interesting way. Some of these results were later presented at relevant industry conferences.
This year we are continuing this experiment but focusing on applications in one sector - retail analytics. In cooperation with students from the universities of Warsaw and Lodz, as well as partners from McKinsey and Shumee, this ebook has been created - presenting various ideas and applications on how to use predictive modelling in retail, but also how to enrich these solutions with XAI.
I hope that the presented solutions will trigger development of new interesting solutions implementing explainable machine learning in the retail industry.
## How this book came about
This book is the result of a student projects for [Interpretable Machine Learning](https://github.com/pbiecek/InterpretableMachineLearning2021) course at University of Warsaw and University of Łódź. Each team has prepared one case study for selected XAI technique.
This project is inspired by a fantastic book [Limitations of Interpretable Machine Learning Methods](https://compstat-lmu.github.io/iml_methods_limitations/) done at the Department of Statistics, LMU Munich.
We used the LIML project as the cornerstone for this repository.
## How to build the book
Step 1: Clone or download the repository https://github.com/pbiecek/xai_stories_2.
Step 2: Install dependencies
```
devtools::install_dev_deps()
```
Step 3: Render the book (R commands)
```{r}
bookdown::render_book('./', 'bookdown::gitbook')
```