<p>In the current state, Deep learning for Tabular is not very popular, so it is very hard to find libraries that support feature importance. However, <a href="https://github.com/dreamquark-ai/tabnet" rel="nofollow noreferrer">TabNet</a> also provides the <code>model.features_importance_</code> metrics to be used as usual. <a href="https://www.kaggle.com/code/optimo/tabnet-with-loop-feature-engineering-explained#Basic-Features-importance" rel="nofollow noreferrer">This Kaggle notebook</a> provides an example on how to use just that.</p>

CC BY-SA 4.0
2023-01-19T06:55:15.760Z
2023-01-19T06:55:15.760Z
Minh-Long Luu
<p>For tree methods, I can plot the feature importance plot from <code>tree.feature_importances_</code> in sklearn, is this achievable in deep learning (neural networks)?</p>
<p>Is there a way to see the feature importance in deep learning (neural networks)?</p>

CC BY-SA 4.0
2023-01-19T10:35:40.650Z
Are you asking how to compute this conceptually or are you asking for a library does it? If the latter, then this question is off-topic. If the former and the latter, then it's on-topic. If the former, it's on-topic.
nbro
<neural-networks><deep-learning><decision-trees><feature-selection>
Is there a way to see the feature importance in deep learning (neural networks)?
user900476
# Is there a way to see the feature importance in deep learning (neural networks)?

(Asked by: username_0 on 2023-01-18)

For tree methods, I can plot the feature importance plot from `tree.feature_importances_` in sklearn, is this achievable in deep learning (neural networks)?

Is there a way to see the feature importance in deep learning (neural networks)?

**Tags:** neural-networks, deep-learning, decision-trees, feature-selection

> **Comments:**
> **username_1**: Are you asking how to compute this conceptually or are you asking for a library does it? If the latter, then this question is off-topic. If the former and the latter, then it's on-topic. If the former, it's on-topic.  

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## 1 answers

### Answer by username_2 on 2023-01-19. Score: 1

In the current state, Deep learning for Tabular is not very popular, so it is very hard to find libraries that support feature importance. However, [TabNet](https://github.com/dreamquark-ai/tabnet) also provides the `model.features_importance_` metrics to be used as usual. [This Kaggle notebook](https://www.kaggle.com/code/optimo/tabnet-with-loop-feature-engineering-explained#Basic-Features-importance) provides an example on how to use just that.


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