metadata

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 integration.