Neural networks are known to be better modeling techniques as compared to machine learning tree-based algorithms. Are there any exceptions to this?

  • $\begingroup$ By "tree models", do you mean decision trees and random forests? Or do you mean something else? $\endgroup$ – nbro Aug 1 at 12:19

Hard to say in general. Speaking from my own experience and by looking at which models win Kaggle competitions (see here and here), I would say tree-based models e.g. Random Forests, Decision Trees, Gradient Boosting are favorable over neural networks when working with low-dimensional data and easy interpretable features (usually simple tabular data with numeric, ordinal or categorical features).

Whereas when working with everything high-dimensional like images, text, time series or other data with non-trivial features, I would recommend neural networks.

Of course there might be exceptions and the future may prove me wrong.

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