# Model (machine learning)

> In general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions.[^1]

In general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions.[^1]

> In [supervised machine learning](https://wiki.g15e.com/pages/Supervised%20learning.txt), a model takes an [example](https://wiki.g15e.com/pages/Example%20(machine%20learning.txt)) as input and infers a prediction as output. Within supervised machine learning, models differ somewhat. For example:
>
> - A linear regression model consists of a set of weights and a bias.
> - A neural network model consists of:
>   - A set of hidden layers, each containing one or more neurons.
>   - The weights and bias associated with each neuron.
> - A <decision tree> model consists of:
>   - The shape of the tree; that is, the pattern in which the conditions and leaves are connected.
>   - The conditions and leaves.
>
> You can save, restore, or make copies of a model.
>
> Unsupervised machine learning also generates models, typically a function that can map an input example to the most appropriate cluster.

## Footnotes

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