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--- |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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metrics: |
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- rmse |
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library_name: pytorch |
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datasets: |
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- gvlassis/california_housing |
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pipeline_tag: tabular-regression |
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--- |
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# mlp-california-housing |
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A multi-layer perceptron (MLP) trained on the California Housing dataset. |
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It takes eight inputs: `'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`. It predicts `'MedHouseVal'`. |
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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'. |
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Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/mlp_california_housing.ipynb |
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Experiment tracking: https://wandb.ai/sadhaklal/mlp-california-housing |
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## Usage |
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``` |
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``` |
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## Metric |
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RMSE on the test set: 0.5502 |
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--- |
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This model has been pushed to the Hub using the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. |
