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I'm working at classifying documents according to their content.

First I built a decision tree model that gives 90% of goods results.

Then I tried a TFIDF/SVC approach which also gives 90% of good results.

So now i'd like to combine both. My first thought was to add the prediction of TFIDF/SVC as a feature of the decision tree.

I saw this post about bagging/stacking/boosting. For me, adding the feature to the decision tree is equivalent to stacking. Is that correct ?

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Yes. Stacking is essentially feeding the predictions of the base learners to a meta learner. Sort of like a model of the models. Here's a good explanation of that.

Bagging,Boosting and Stacking

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