Sklearn-genetic-opt
powershap
| Sklearn-genetic-opt | powershap | |
|---|---|---|
| 6 | 1 | |
| 273 | 181 | |
| - | 4.4% | |
| 4.6 | 3.5 | |
| 8 days ago | 12 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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Sklearn-genetic-opt
- GitHub - rodrigo-arenas/Sklearn-genetic-opt: Hyperparameters tuning and feature selection, using evolutionary algorithms.
- New Python AutoML Package
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Looking for contributors AutoML project in Python
The project is open for collaborators of different levels of expertise, there are some issues about new features, enchacements on docs, etc. Repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt
- I've been working on an machine learning hyperparameters tuning open source project
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Looking for open source contributors: AutoML
Here is the repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt
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Introducing Sklearn-genetic-opt: Hyperparameters tuning using evolutionary algorithms [project]
If you want to know more the details or contribute, you can check the Github repository
powershap
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[R] PowerShap: A power-full Shapley feature selection method.
We are glad to present our novel shap-based wrapper feature selection method called PowerShap! This method uses statistical hypothesis testing and power calculations on Shapley values, enabling fast and intuitive wrapper-based feature selection. The complete library and methods are fully compatible with Sklearn, LightGBM, CatBoost, and more are coming in further following releases and the library can be found here: https://github.com/predict-idlab/powershap! The library is open-source and usable out-of-the-box as shown in the video!
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