---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
metrics:
- rmse
library_name: pytorch
datasets:
- gvlassis/california_housing
pipeline_tag: tabular-regression
---

# mlp-california-housing

A multi-layer perceptron (MLP) trained on the California Housing dataset.

It takes eight inputs: `'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`. It predicts `'MedHouseVal'`.

It is a PyTorch adaptation of the TensorFlow model in Chapter 10 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'.

Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/mlp_california_housing.ipynb

Experiment tracking: https://wandb.ai/sadhaklal/mlp-california-housing

## Usage

```

```

## Metric

RMSE on the test set: 0.5502

---

This model has been pushed to the Hub using the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.