A very interesting ensemble solution is offered by the class VotingClassifier, which isn't an actual classifier but a wrapper for a set of different ones that are trained and evaluated in parallel. The final decision for a prediction is taken by majority vote according to two different strategies:
- Hard voting: In this case, the class that received the major number of votes, Nc(yt), will be chosen:
- Soft voting: In this case, the probability vectors for each predicted class (for all classifiers) are summed up and averaged. The winning class is the one corresponding to the highest value:
Let's consider a dummy dataset and ...
