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I'm sure you can use dropout in any parameterized model, but I suspect it'll only really be helpful if you have enough parameters/nodes. Also dropout in neural nets has a Bayesian meaning, Yarin Gal for example has done lots of work on this. In your decision tree example, I believe you're talking about pruning, which is different. In that context you're ...


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Sorry if this is a bad use of answer to add comment but since my reputation is not high enough this is only way to leave a comment to OP's question. I think some of the answers misunderstood the OP's intention. Over fitting is used as a means to test the complexity of the model - if a model cannot overfit a small dataset then it's likely not able to ...


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I'll try to answer on more general questions Is it ok that model performs better on validation, then on train? It's certainly fine if you use techniques like dropout or data augmentation and the difference is not that big. Because in case of dropout for train you use part of the network, and for validation the whole. I'm suspicious my model is too good. ...


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