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These are generally known as ensemble methods. Your method is essentially what Scikit-Learn's VotingClassifier does, which is perfectly reasonable and might give you better results. Of course, if you have an ensemble of classifiers and some of them perform quite poorly, the ensemble might not be able to beat the best classifier: you'll need to check this in ...


2

In my opinion, it is not because ensemble methods are not good, just the state-of-the-art and Kaggle competitions are two different fields. Kaggle competitions can be understood as an industry project where the target (accuracy, distance value, etc) is the most important, and they can select some computationally expensive way such as ensemble methods to ...


1

For the ANN, it should be the average of the error per instance from testing (prediction) when each instance is left out of training. ANNs can unfortunately learn based on the order of instances used for training, so it helps to train/test and then shuffle (permute, or randomly re-order) and then assign to k-folds, then train/test again in order to prevent ...


1

It is a simple way to do it but it is not wrong. If you are getting probabilities for each model, then, you can average them. Then, you can do the classification. Also, you assign weights to each model based on a validation set and regressing the weights for each models prediction.


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