<p>I'm not aware of a direct way for finding the best NN architecture for a given task, but the recommended way, as far as I know, is to devise a network that can overfit the training data, and then apply regularization on top of it.</p>
<p>That way, you can be almost sure you're not underfitting/underperforming due to network capacity.</p>

CC BY-SA 4.0
2021-03-31T02:51:52.447Z
2021-03-31T13:41:59.803Z
2021-03-31T13:41:59.803Z
SpiderRico
<p>It's harder to overfit it certainly!</p>

<p>I mean practically speaking there has to be some assumptions on the generation model of your data, either explicit or implicit.</p>

<p>I would try probably 1-2 layer network first(maybe your data is linearly separable if you're lucky). </p>

CC BY-SA 4.0
2020-03-05T21:34:18.527Z
2020-03-05T21:34:18.527Z
FourierFlux
<p>I'm currently trying to predict 1 output value with 52 input values. The problem is that I only have around 100 rows of data that I can use. </p>

<p>Will I get more accurate results when I use a small architecture than when I use multiple layers with a higher amount of neurons? </p>

<p>Right now, I use 1 hidden layer with 1 neuron, because of the fact that I need to solve (in my opinion) a basic regression problem. </p>

<neural-networks><ai-design><datasets><regression><architecture>
Is a basic neural network architecture better with small datasets?
Yari Nowicki
# Is a basic neural network architecture better with small datasets?

(Asked by: username_0 on 2020-03-05)

I'm currently trying to predict 1 output value with 52 input values. The problem is that I only have around 100 rows of data that I can use.

Will I get more accurate results when I use a small architecture than when I use multiple layers with a higher amount of neurons?

Right now, I use 1 hidden layer with 1 neuron, because of the fact that I need to solve (in my opinion) a basic regression problem.

**Tags:** neural-networks, ai-design, datasets, regression, architecture


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## 2 answers

### Answer by username_1 on 2021-03-31. Score: 1

I'm not aware of a direct way for finding the best NN architecture for a given task, but the recommended way, as far as I know, is to devise a network that can overfit the training data, and then apply regularization on top of it.

That way, you can be almost sure you're not underfitting/underperforming due to network capacity.


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### Answer by username_2 on 2020-03-05. Score: 0

It's harder to overfit it certainly!

I mean practically speaking there has to be some assumptions on the generation model of your data, either explicit or implicit.

I would try probably 1-2 layer network first(maybe your data is linearly separable if you're lucky).


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