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# Tweet summary
PDP (Partial Deference Plot)
- Assumption: Fix all other variables, generate simulation on target variables
- Biggest issue: non-realistic parameter combination, i.e. 200cm 40kg
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WDYT? Is this publication in scope?
```
@inproceedings{Benti_2024,
author = {Benti, Donayam and Austin, Todd},
booktitle = {2024 International Symposium on Secure and Private Execution Environment D…
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These comments on the sections related to black box models made on the version which was live on Friday 8 November 2024. The sections outlined below are what was covered.
**Definitions**
- [ ] A…
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**TODO**
- [x] preproc.py / modeling.py refactor @AbnerTeng
- [ ] README.md @AbnerTeng
- [ ] models transfer to onnx type @AbnerTeng
- [ ] shell script interface enhancement @AbnerTeng
- [ …
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Hello Jay,
A great video as usual. Explainable AI is a critical component to bring the AI revolution to many business areas.
I thought you might be interested in this opensource package: https://g…
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- [x] Add category
- [x] Update category:
**Category details:**
Currently, the distribution of projects among categories is very uneven.
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Active learning 4 projects
Bio…
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Hey! I just saw your paper and I was curious if you compared this to Explainable Boosting Machines.
From what I can see from your description, you're using SHAP with bagging on XGBoost models. The EB…
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Task.
- Implement Explainable AI for 'Linear regression' over existing and new models.
- Can take any sample use case data from [Scikit-learn examples.](https://scikit-learn.org/stable/auto_examples…
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Task.
- Implement Explainable AI for 'Logistic regression' over existing and new models.
- Can take any sample use case data from [Scikit-learn examples.](https://scikit-learn.org/stable/auto_exampl…
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Task.
- Implement Explainable AI for 'Decision trees/Random Forest' over existing and new models.
- Can take any sample use case data from [Scikit-learn examples.](https://scikit-learn.org/stable/au…