I have an array of sklearn.tree._classes.DecisionTreeClassifier classifiers that are used in a boosting algorithm, so the final classifier is a weighted sum of these individual trees. The problem is that for out-of-sample prediction, it takes a long time to apply the final classifier.

Is there an effective way to combine 2 DecisionTreeClassifiers into an equivalent single DecisionTreeClassifier so that oos prediction does not require iteration?

I could inductively fit a single tree classifier to the ensemble classifier and hope for a good fit - any exact methods without a fitting step?