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In French..
- [x] attention imports circulaires
- [x] `import *` en dehors du `__init__.py` à proscrire
- [x] manque des listes de méthodes `__all__` dans les fichiers
- [x] naming des fichiers …
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The first thing that I think needs to happen is to add the capacity to save ensemble members for ALL INPUTS, not just met drivers. I believe the code that is sampling is already generalized to read t…
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Add Jupyter Notebook examples on US simulations of particle in a model potential (in OpenMM), for:
- [x] 1D US
- [x] 1D to phi-ensemble reweighting
- [x] 1D to 2D reweighting
- [ ] 2D US
- [ ] …
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## Feature Request:
Implementing stratified sampling in ERT to improve the sampling process. Stratified sampling could potentially provide a better coverage of the sample space and reduce the risk o…
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Due to the current implementation of BBI, the adaptation of ML model such as DnnLearner and ModifiedRF is part of the main library.
I would argue to keep these modules outside of the main library …
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I'd like to take a crack at implementing support for sampling from BEEF ensembles (assuming you'd accept the PR 😉 ) – should be pretty simple, see e.g. [here](https://github.com/rosswhitfield/ase/blob…
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Support weighted feature sampling in cuML RF similar to sklearn.
eg:
https://github.com/scikit-learn/scikit-learn/blob/1495f69242646d239d89a5713982946b8ffcf9d9/sklearn/ensemble/forest.py#L217
s…
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> **New Functions**
- [ ] Explore variable selection based on univariate model performance, see [Oeser et al., 2024](https://onlinelibrary.wiley.com/doi/10.1111/geb.13911)
- [ ] New function to calc…
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While imbalanced-learn 0.X really focuses on samplers, over time we start to add additional methods like ensemble classifiers. We could think about releasing imbalanced-learn 1.X which could reorganiz…
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For reasons of increasing SDA stability, interpreting parameter and driver weights, and possibly reducing process error, there are advantages of running ensemble adjustment (nudging forecast prior & c…