CODEWITHSUNDEEP
pythonmachine-learningscikit-learn
user3666471
user3666471

Reputation: 945

Ensemble learning with 2 classifiers

I'm trying to combine 2 approaches to classifying my data, one comes from a SVM and another external classifier that gives out one or more labels as to what it thinks the observation point is. Is it possible to get these two classifiers to work together? Similar to what boosting does.

I've noticed that in scikit's adaboost implementation, it takes in only 1 type of classifier. Further, the 2nd classifier that I have, which gives out 1 or more labels doesn't have any "weights" associated with it. How do I go about this?

Upvotes: 0

Views: 1536

Answers (1)

Kyle Kastner
Kyle Kastner

Reputation: 1018

There is a technique called stacked generalization, which basically takes the output of any K classifiers and trains a second layer classifier on top of this - so the input to the second layer classifier is the output of the first layer classifier. You can either use crossvalidated and averaged label predictions, or predict_proba outputs, or some other metric.

A nice introductory link on this is here

Upvotes: 3

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