# When to use regression vs neural network model

**URL:** <https://community.deeplearning.ai/t/when-to-use-regression-vs-neural-network-model/416964>\
**Category:** Advanced Learning Algorithms\
**Tags:** week-module-3\
**Created:** [August 14, 2023, 4:52am UTC](https://community.deeplearning.ai/t/when-to-use-regression-vs-neural-network-model/416964 "2023-08-14T04:52:30Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![James\_Goff](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/james_goff/32/59403_2.png) [@James\_Goff](https://community.deeplearning.ai/u/James_Goff)\
**Post date:** [August 14, 2023, 4:52am UTC](https://community.deeplearning.ai/t/when-to-use-regression-vs-neural-network-model/416964/1 "2023-08-14T04:52:30Z")

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So the lecture explains how to choose polynomial features for regression and how to a choose neural network architecture based on MSE. But I have a more fundamental question. For any given supervised ML problem, how do you know whether to use a simple regression model or a neural network? If this is already addressed in lecture I apologize; I wasn’t able to find a clear answer to the question.

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**Author:** ![TMosh](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/tmosh/32/9321_2.png) [@TMosh](https://community.deeplearning.ai/u/TMosh)\
**Post date:** [August 14, 2023, 5:51am UTC](https://community.deeplearning.ai/t/when-to-use-regression-vs-neural-network-model/416964/2 "2023-08-14T05:51:59Z")

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Start with the simplest possible model. If you don’t get “good enough” results, then start adding complexity.

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**Author:** ![James\_Goff](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/james_goff/32/59403_2.png) [@James\_Goff](https://community.deeplearning.ai/u/James_Goff)\
**Post date:** [August 14, 2023, 6:24am UTC](https://community.deeplearning.ai/t/when-to-use-regression-vs-neural-network-model/416964/3 "2023-08-14T06:24:29Z")

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Is there any way to know if a model won’t be good enough without actually having to test it?

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**Author:** ![Mohamed-Amine\_BENHIM](https://avatars.discourse-cdn.com/v4/letter/m/bc8723/32.png) [@Mohamed-Amine\_BENHIM](https://community.deeplearning.ai/u/Mohamed-Amine_BENHIM)\
**Post date:** [August 14, 2023, 9:26am UTC](https://community.deeplearning.ai/t/when-to-use-regression-vs-neural-network-model/416964/4 "2023-08-14T09:26:08Z")

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What I’ve learned, is regression and NN are different types of models, regression is good but is not good enough, it has some disadvantages:  
you need to do manual feature engineering in contrast NNs do that automatically  
also one major point that if you collect more data a large data and train your regression model on it you will find that the accuracy doesn’t getting better, it’s like that regression models have a limit level even if you have more data, in contrast in NNs if you have large data you can simply use a complex NN and you will find that the accuracy of the model getting better.  
and finally in regression models they don’t support transfer learning.  
simply always use NNs regression models are a basics models.  
and good luck.

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<div class="post-metadata">

**Author:** ![TMosh](https://sea1.discourse-cdn.com/dlai/user_avatar/community.deeplearning.ai/tmosh/32/9321_2.png) [@TMosh](https://community.deeplearning.ai/u/TMosh)\
**Post date:** [August 14, 2023, 2:44pm UTC](https://community.deeplearning.ai/t/when-to-use-regression-vs-neural-network-model/416964/5 "2023-08-14T14:44:45Z")

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> [@James\_Goff](#):
>
> Is there any way to know if a model won’t be good enough without actually having to test it?

No.