<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>

CC BY-SA 4.0
2020-08-02T07:45:09.773Z
2020-08-02T07:45:09.773Z
Tinu
<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>

<neural-networks><comparison><decision-trees><random-forests><gradient-boosting>
What are some applications where tree models perform better than neural networks?
jaiswati_b
# 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


---

## 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.


---

