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What classes of estimators does EnbPI in MAPIE 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.
The text was updated successfully, but these errors were encountered:
MAPIE works for all sklearn-compatible estimator classes. To this end, EnbPI already works with any regression model classes including boosted trees (CatBoost/XGBoost/LightGBM) and scikit-learn regressors.
In conclusion, MAPIE is not limited to bagging estimators, it can work with any model and there are no gap in the implementation compared with the model presented in the article.
What classes of estimators does EnbPI in MAPIE 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.
The text was updated successfully, but these errors were encountered: