{
  "fetched_at": "2026-10-24T17:34:55Z",
  "source_url": "https://www.kaggle.com/competitions/gayest-competition-ever/data",
  "pages": [
    {
      "name": "Evaluation",
      "content": "Submissions are evaluated using the multi-class logarithmic loss. Each `id` in the test set had a single true class label, `Status`. For each `id`, you must submit a set of predicted probabilities for each of the three possible outcomes, e.g., `Status_C`, `Status_CL`, and `Status_D`.\n\nThe metric is calculated\n\n$$log loss = -\\frac{1}{N}\\sum_{i=1}^N\\sum_{j=1}^My_{ij}\\log(p_{ij}),$$\n\nwhere \\\\(N\\\\) is the number of rows in the test set, \\\\(M\\\\) is the number of outcomes (i.e., 3),  \\\\(log\\\\) is the natural logarithm, \\\\(y_{ij}\\\\) is 1 if row \\\\(i\\\\) has the ground truth label \\\\(j\\\\) and 0 otherwise, and \\\\(p_{ij}\\\\) is the predicted probability that observation \\\\(i\\\\) belongs to class \\\\(j\\\\).\n\nThe submitted probabilities for a given row are not required to sum to one because they are rescaled prior to being scored (each row is divided by the row sum). In order to avoid the extremes of the log function, predicted probabilities are replaced with \\\\(max(min(p,1-10^{-15}),10^{-15})\\\\).\n\n## Submission File\nFor each `id` row in the test set, you must predict probabilities of the three outcomes `Status_C`, `Status_CL`, and `Status_D` . The file should contain a header and have the following format:\n\n    id,Status_C,Status_CL,Status_D\n    7905,0.628084,0.034788,0.337128\n    7906,0.628084,0.034788,0.337128\n    7907,0.628084,0.034788,0.337128\n    etc.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't shit!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "The dataset for this competition (both train and test) was generated from a deep learning model trained on the [Cirrhosis Patient Survival Prediction](https://www.kaggle.com/datasets/joebeachcapital/cirrhosis-patient-survival-prediction) dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.\n\n## Files\n\n*   **train.csv** - the training dataset; `Status` is the categorical target; `C` (censored) indicates the patient was alive at `N_Days`, `CL` indicates the patient was alive at `N_Days` due to liver a transplant, and `D` indicates the patient was deceased at `N_Days`.\n*   **test.csv** - the test dataset; your objective is to predict the probability of each of the three `Status` values, e.g., `Status_C`, `Status_CL`, `Status_D`.\n*   **sample_submission.csv** - a sample submission file in the correct format",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "abstract",
      "content": "**Welcome to the 2023 Kaggle Playground Series!** Thank you to everyone who participated in and contributed to Season 3 Playground Series so far. This is our last episode for the Season 3 and we wish you all a Happy New Year! Stay tuned for the new season next year!\n\n**Your Goal:** For this Episode of the Series, your task is to use a multi-class approach to predict the the outcomes of patients with cirrhosis. Good luck!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}