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Currently, AdaBoost models from scikit-learn [1] are implicitly ignored by conifer's sklearn converter [2]. Neither of the existing BDT or RF converter methods work "out of the box" for AdaBoostClassifier, which names some attributes differently to the other methods. A new converter method will be needed. But since the base classifiers are DecisionTrees, it should not be a major addition.
Currently, AdaBoost models from scikit-learn [1] are implicitly ignored by conifer's sklearn converter [2]. Neither of the existing BDT or RF converter methods work "out of the box" for AdaBoostClassifier, which names some attributes differently to the other methods. A new converter method will be needed. But since the base classifiers are DecisionTrees, it should not be a major addition.
[1] https://github.com/scikit-learn/scikit-learn/blob/0.21.3/sklearn/ensemble/weight_boosting.py#L292
[2] https://github.com/thesps/conifer/blob/master/conifer/converters/sklearn.py#L42-L46
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