4

You should think of them as different approaches. A deep neural net is a single independent model, whereas ensemble models are ensembles of many independent models. The primary connection between the two is dropout, a particular method of training deep neural nets that's inspired by ensemble methods.


3

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

Deep neural networks could - in principle - be a component of an ensemble of machine learning algorithms, yes. Ensemble method basically just means use multiple algorithms and combining their output somehow. Other than that, I don't see any special connection between deep learning and the idea of ensemble methods. DL is just one more tool in the toolkit....


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 ...


2

Goodness is subjective. Reliable knowledge isn't possible with that flimsy a quality objective. The sturdy objective criteria you gave is 95%, so it is bad by that criteria. (I'm assuming that the 95% is expected for a given data set or a randomized sample from a given data set.) However, the 80% accuracy is good by the criteria where the you sum the ...


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.


1

Not in terms of models, but there is a terminology called 'Hierarchical learning', wherein if your model has a task to classify disease, then, If it detects a presence of a disease (disease/ no disease), then it proceeds to further classify a disease(class A/B/C/...). Else it does not proceed. This technique of hierarchical learning is very common amongst ...


1

? This means that there are not promising versions of this algorithm fro regression until 2012. After your question, I have found one of the survey research paper which is done or ensemple methods for regression. This table also extracted from this paper. Read this paper, it will help you a lot more This one is latest paper published on object detection with ...


Only top voted, non community-wiki answers of a minimum length are eligible