[Feature Request]: - EnbPI for any regression model

[valeman]

Module

Regression

Contact Details

No response

Feature Request

What classes of estimators does EnbPI in PUNCC works with?

The tutorial mentions RandomForest, the EnbPI model as such as published in paper is not limited to bagging estimators and it can work with any model.

Is there a gap in implementation vs the model in the paper?

If so, it would be good to have EnbPI work with any regression model classes including boosted trees (CatBoost/XGBoost/LightGBM) and scikit-learn regressors.

A minimal example

No response

Version

v0.9

Environment

- OS:
- Python version:
- Packages used version:

[M-Mouhcine]

Hi @valeman,

Puncc enables virtually any underlying learning algorithm and aggregation function for EnbPI, including neural networks (pytorch, tf ...), ensemble methods, ... as long as we correctly wrap them with a suitable wrapper (usually puncc.deel.api.prediction.BasePredictor). Here is an synthetic example using different models you can open in colab .

Let me know if I understood and answered correctly your question.

[valeman]

That’s great let me check it out