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I have been working on a project as a part of my studies(computer/data science). I tried to make the best classifier I can with what I learned, and recently I have tried to upgrade this classifier using new things I learned. I have tried using several ensemble methods such as bagging, pasting and voting, and the results I get are similar to using a single classifier or even a bit worse, but taking more time to run.

My question is, in which cases should you use ensemble learning, regarding to data size, data kind(text/images)?

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When your model has better performance in training data than in validation data, it makes sense to ensemble in order to reduce the variance of the final model.

If the model doesn't overfit at all, I don't think it makes sense.

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