{
  "fetched_at": "2026-10-19T23:30:25Z",
  "source_url": "https://www.kaggle.com/competitions/ch315-2020/data",
  "pages": [
    {
      "name": "data-description",
      "content": "# File description \n\n* `features.csv` : contains all feature columns and is in the same format as the files we used for training the models\n* `submission.csv`: example of a submission file ",
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    {
      "name": "Description",
      "content": "This is the competition for the CH315 machine learning workshop. \n\nTry to build the best models to predict the high-pressure carbon dioxide uptake in MOFs!\nWe didn't provide you with all the data---we use this data to test the generalization performance of the model. \n\n## Getting started\n1. Download the file with the features (they are also in the `data` folder on GitHub) \n2. Use your model to make predictions and create a submission `.csv`\n3. Upload your prediction\n\nYour submissions will be evaluated based on the mean absolute error on the subset from the same [database you used for training](https://www.nature.com/articles/s41586-019-1798-7#article-comments). \n\nFeel free to explore any model architecture you wish---the only goal here is to get the best prediction with the given features. \n\n## Acknowledgment\nWe thank Leopold Talirz for incredibly valuable feedback on the exercises.",
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    {
      "name": "Evaluation",
      "content": "You'll be scored on the mean absolute error (MAE) on a holdout test set ",
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      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "Have fun in this competition!",
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