Materials for ML interpretability Code Club workshop
| Topic | Simple interpretability methods for black-box machine learning systems |
| Presenter | Dr. Adriano Soares Koshiyama |
| Date | Wednesday, 16 September 2020 |
| Length | 60 mins |
| Language | python |
| Libraries | pandas, numpy, sklearn, matplotlib, seaborn |
| Software used | Jupyter Notebook |
| File | Description |
|---|---|
| Interpretability Code Club.pdf | Presentation file |
| NotebookInterpretability.ipynb | Notebook file for interactive coding session |
| mortgage_data_balanced.csv | Data for interactive coding session |
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