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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?
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In stacking, an algorithm takes the outputs of sub-models as input and attempts to learn how to best combine the input predictions to make a better output prediction.
The difference between stacking and blending is that Stacking uses out-of-fold predictions for the train set of the next layer (i.e meta-model), and Blending uses a validation set (let's say, 10-15% of the training set) to train the next layer.
Voting Classifier is a machine-learning algorithm often used by Kagglers to boost the performance of their model and climb up the rank ladder. Voting Classifier can also be used for real-world datasets to improve performance, but it comes with some limitations.
Stacking is one of the most popular ensemble machine learning techniques used to predict multiple nodes to build a new model and improve model performance. Stacking enables us to train multiple models to solve similar problems, and based on their combined output, it builds a new model with improved performance.
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 my k classifiers" you create another abstraction layer, where you actually learn to predict the correct label having k votes.
In short terms, basic voting/stacking/grading methods can be outlined as:
v, that given answers a_1,...,a_k results in a=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 itk classifiers to predict its "classification grade" for current point, and use it to make decision
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