# Parameter (machine learning)

> The weights and biases that a model learns during training. For example, in a linear regression model, the parameters consist of the bias ($b$) and all the weights ($w_1$, $w_2$, and so on) in the following formula:

The [weights](https://wiki.g15e.com/pages/Weight%20(machine%20learning.txt)) and [biases](https://wiki.g15e.com/pages/Bias%20(machine%20learning.txt)) that a model learns during [training](https://wiki.g15e.com/pages/Training%20(machine%20learning.txt)). For example, in a [linear regression](https://wiki.g15e.com/pages/Linear%20regression.txt) model, the parameters consist of the bias ($b$) and all the weights ($w_1$, $w_2$, and so on) in the following formula:

$$
y' = b + w_1 x_1 + w_2 x_2 + \cdots + w_n x_n
$$

In contrast, [hyperparameters](https://wiki.g15e.com/pages/Hyperparameter.txt) are the values that *you* (or a hyperparameter tuning service) supply to the model. For example, [learning rate](https://wiki.g15e.com/pages/Learning%20rate.txt) is a hyperparameter.[^1]

## Footnotes

[^1]: https://developers.google.com/machine-learning/glossary#parameter