# Multi Layer Perceptron

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MLP is normally used for Classification. This case is explained at [multilayer-perceptron](../classification-algorithms/multilayer-perceptron "mention")
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* MLP can be used for regression
* It can fit the dataset better than the [linear-regression](linear-regression "mention") model

<figure><img src="https://569842953-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LmjpNbCRLUyGiAxD8kn%2Fuploads%2FZo1c5Dy1Q0pHXkcOq6Z1%2Fimage.png?alt=media&#x26;token=bb0f00f5-d701-460e-8468-537405457d5a" alt=""><figcaption></figcaption></figure>

* The architecture of a MLP for regression is simpler, with **only one node on the output layer**.
* The loss function used is: [#mse-loss-l2-regularization](../../../../ml-techniques/loss-functions#mse-loss-l2-regularization "mention").

<figure><img src="https://569842953-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LmjpNbCRLUyGiAxD8kn%2Fuploads%2Fjuw4p1n9hjch341tU9LL%2Fimage.png?alt=media&#x26;token=ff35b6b7-046d-4fc2-973c-38645bd341fb" alt=""><figcaption></figcaption></figure>
