<p>I assume that by &quot;tuning parameter&quot; you mean, e.g., the weights of a nueral net. These are parameters that can be learned from data. A hyperparameter, however, can not (or only indirectly) be estimated from your training data. Even though, during hyperparameter optimization you can determine which of these hyperparameters results in a model that is more (or less) appropriate to describe the data -- or you can simply make an educated guess about that parameter. In this sense, the polynomial degree of a regression model is a hyperparameter that needs to be determined before you use the model for training it and, e.g., making predictions or inference with it.</p>

CC BY-SA 4.0
2023-10-09T14:41:27.023Z
2023-10-09T14:41:27.023Z
BanDoP
<p>We know that in ML we have tuning parameters and hyperparameters.
Is model order of a model class (for example, polynomial regression class) a hyperparameter or a tuning parameter?</p>

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2023-10-09T21:56:56.073Z
What do you mean by "model order of a model class"?
nbro
<machine-learning><validation>
Is model order of a model class (for example, polynomial regression class) a hyperparameter or a tuning parameter?
DSPinfinity
# Is model order of a model class (for example, polynomial regression class) a hyperparameter or a tuning parameter?

(Asked by: username_0 on 2023-10-09)

We know that in ML we have tuning parameters and hyperparameters.
Is model order of a model class (for example, polynomial regression class) a hyperparameter or a tuning parameter?

**Tags:** machine-learning, validation

> **Comments:**
> **username_1**: What do you mean by "model order of a model class"?  

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## 1 answers

### Answer by username_2 on 2023-10-09. Score: 1 | Accepted answer

I assume that by "tuning parameter" you mean, e.g., the weights of a nueral net. These are parameters that can be learned from data. A hyperparameter, however, can not (or only indirectly) be estimated from your training data. Even though, during hyperparameter optimization you can determine which of these hyperparameters results in a model that is more (or less) appropriate to describe the data -- or you can simply make an educated guess about that parameter. In this sense, the polynomial degree of a regression model is a hyperparameter that needs to be determined before you use the model for training it and, e.g., making predictions or inference with it.


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