{
  "fetched_at": "2026-10-20T01:39:43Z",
  "source_url": "https://www.kaggle.com/competitions/ai-and-machine-learning/data",
  "pages": [
    {
      "name": "Evaluation",
      "content": "Submissions are scored based on the quadratic weighted kappa, which measures the agreement between two outcomes. This metric typically varies from 0 (random agreement) to 1 (complete agreement). In the event that there is less agreement than expected by chance, the metric may go below 0.\n\nThe quadratic weighted kappa is calculated as follows. First, an N x N histogram matrix <em>O</em> is constructed, such that <em>O<sub>i,j</sub></em> corresponds to the number of `Id`s <em>i</em> (actual) that received a predicted value <em>j</em>. An <em>N-by-N </em>matrix of weights, <em>w</em>, is calculated based on the difference between actual and predicted values:\n\n$$w_{i,j} = \\frac{\\left(i-j\\right)^2}{\\left(N-1\\right)^2}$$\n\nAn <em>N-by-N</em> histogram matrix of expected outcomes, <em>E</em>, is calculated assuming that there is no correlation between values.  This is calculated as the outer product between the actual histogram vector of outcomes and the predicted histogram vector, normalized such that <em>E</em> and <em>O</em> have the same sum.\n\nFrom these three matrices, the quadratic weighted kappa is calculated as: \n\n$$\\kappa=1-\\frac{\\sum\\_{i,j}w\\_{i,j}O\\_{i,j}}{\\sum\\_{i,j}w\\_{i,j}E\\_{i,j}}.$$\n\n\n## Submission File\nFor each `Id` in the test set, you must predict the value for the target `quality`. The file should contain a header and have the following format:\n\n    Id,quality\n    2056,5\n    2057,7\n    2058,3\n    etc.\n\n",
      "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 cheat!\n - Apply yourself!\n - Have fun!\nDON'T USE CHATGPT TO CODE< IF YOU use Chatgpt you might be disqualified for this round",
      "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 [Wine Quality dataset](https://www.kaggle.com/datasets/yasserh/wine-quality-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; `quality` is the target (ordinal, integer)\n*   **test.csv** - the test dataset; your objective is to predict `quality`\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! \n\n**Your Goal:** For this Episode of the Series, your task is to use regression to predict the quality of wine based on various properties. Good luck!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}