Answers.Body: <p>Hard to say in general. Speaking from my own experience and by looking at which models win Kaggle competitions (see <a href="https://ndres.me/kaggle-past-solutions/" rel="nofollow noreferrer">here</a> and <a href="https://www.kaggle.com/rajiao/winning-solutions-of-kaggle-competitions" rel="nofollow noreferrer">here</a>), I would say tree-based models e.g. Random Forests, Decision Trees, Gradient Boosting are favorable over neural networks when working with <strong>low-dimensional data</strong> and easy <strong>interpretable features</strong> (usually simple tabular data with numeric, ordinal or categorical features).</p>
  <p>Whereas when working with everything high-dimensional like images, text, time series or other data with non-trivial features, I would recommend neural networks.</p>
  <p>Of course there might be exceptions and the future may prove me wrong.</p>
  
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Answers.CreationDate: 2020-08-02T07:45:09.773Z
Answers.LastActivityDate: 2020-08-02T07:45:09.773Z
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Body: <p>Neural networks are known to be generally better modeling techniques as compared to tree-based models (such as decision trees). Are there any exceptions to this?</p>
  
Tags: <neural-networks><comparison><decision-trees><random-forests><gradient-boosting>
Title: What are some applications where tree models perform better than neural networks?
UserDisplayName: jaiswati_b
ThreadText: # What are some applications where tree models perform better than neural networks?
  
  (Asked by: username_0 on 2020-08-01)
  
  Neural networks are known to be generally better modeling techniques as compared to tree-based models (such as decision trees). Are there any exceptions to this?
  
  **Tags:** neural-networks, comparison, decision-trees, random-forests, gradient-boosting
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-08-02. Score: 1
  
  Hard to say in general. Speaking from my own experience and by looking at which models win Kaggle competitions (see [here](https://ndres.me/kaggle-past-solutions/) and [here](https://www.kaggle.com/rajiao/winning-solutions-of-kaggle-competitions)), 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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