I'm writing a machine-learning solution for a problem that may have more than one possible classifier, depending on the data. so I've collected several classifiers, each of them performs better than the others on some conditions. I'm looking into the meta-classification strategies, and I see there are several algorithms. can anyone please point at fundamental difference between them?
what is the difference between the stacking grading, and voting algorithms?
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Voting algorithms are simple strategies, where you aglomerate results of classifiers' decisions by for example taking the class which appears in most cases. Stacking/grading strategies are generalizations of this concept. Instead of simply saying "ok, I have a scheme
v, which I will use to select the best answer among mykclassifiers" you create another abstraction layer, where you actually learn to predict the correct label havingkvotes.In short terms, basic voting/stacking/grading methods can be outlined as:
v, that given answersa_1,...,a_kresults ina=v(a_1,...,a_k)(x_i,y_i)you get(a_i_1,...,a_i_k)and so create the training sample((a_i_1,...,a_i_k),y_i)and train meta-classifier on itkclassifiers to predict its "classification grade" for current point, and use it to make decision