<p>From <a href="https://ai.stackexchange.com/a/28024/18758">this</a> answer, stability is attributed to a learning algorithm</p>
<blockquote>
<p>A <strong>stable learning algorithm</strong> is one for which the prediction does not
change much when the training data is modified slightly.</p>
</blockquote>
<p>At some other places, I read the phrase &quot;stability of neural network model&quot;. I am not sure whether the stability of a learning algorithm and the stability of a model are the same or not. If same then</p>
<blockquote>
<p>A <strong>stable model</strong> is one for which the prediction does not
change much when the training data is modified slightly.</p>
</blockquote>
<p>Is it true? If not, is there anything called stability of a model and is different from the stability of a learning algorithm?</p>
<p>Suppose I am training a neural network model with a gradient descent algorithm. For which one, do I need to attribute stability or instability? Is it to the neural network model or to the gradient descent training algorithm? Or it should be attributed to the combination of both?</p>

CC BY-SA 4.0
2021-08-09T00:18:29.647Z
is one for which the prediction does not change much when the training data is modified slightly. Is example back to do it again
Rachrachben
CC BY-SA 4.0
2021-08-15T23:50:55.340Z
@Rachrachben Do you mean it applies to both model and training algorithm?
hanugm
CC BY-SA 4.0
2021-10-09T12:13:37.277Z
Can you provide a source for the second part(stable model) as well?  The answer you refer to cites a Wikipedia article which gives a list of [stable learning algorithms](https://en.wikipedia.org/wiki/Stability_(learning_theory)#Algorithms_that_are_stable). Neither gradient descent nor neural networks are on the list.
serali
<neural-networks><training><stability>
Is stability an attribute of model or training algorithm used or combination of both?
hanugm
# Is stability an attribute of model or training algorithm used or combination of both?

(Asked by: username_0 on 2021-08-07)

From [this](https://ai.stackexchange.com/a/28024/18758) answer, stability is attributed to a learning algorithm

> A **stable learning algorithm** is one for which the prediction does not
> change much when the training data is modified slightly.

At some other places, I read the phrase "stability of neural network model". I am not sure whether the stability of a learning algorithm and the stability of a model are the same or not. If same then

> A **stable model** is one for which the prediction does not
> change much when the training data is modified slightly.

Is it true? If not, is there anything called stability of a model and is different from the stability of a learning algorithm?

Suppose I am training a neural network model with a gradient descent algorithm. For which one, do I need to attribute stability or instability? Is it to the neural network model or to the gradient descent training algorithm? Or it should be attributed to the combination of both?

**Tags:** neural-networks, training, stability

> **Comments:**
> **username_1**: is one for which the prediction does not change much when the training data is modified slightly. Is example back to do it again  
> **username_0**: @username_1 Do you mean it applies to both model and training algorithm?  
> **username_2**: Can you provide a source for the second part(stable model) as well?  The answer you refer to cites a Wikipedia article which gives a list of [stable learning algorithms](https://en.wikipedia.org/wiki/Stability_(learning_theory)#Algorithms_that_are_stable). Neither gradient descent nor neural networks are on the list.  

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## 0 answers

