{
  "fetched_at": "2026-10-22T09:51:56Z",
  "source_url": "https://www.kaggle.com/competitions/data-mining-programming-hw1/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Please see the data files on Canvas\n\n",
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      "postTitle": "",
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    },
    {
      "name": "Description",
      "content": "Please use the Canvas description document",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MacroFScore\"> Macro F1-Score</a>. The F1 score, commonly used in information retrieval and multi class classification problems, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other. The Macro F-1 score is equivalent to the sklearn.metrics.f1_score with average=\"macro\"</p>\n<h2>Submission Format</h2>\n<p>The submission files should contain two columns: ImageID and Class. \n</p><p>The file should contain a header and have the following format:</p>\n<pre>ImageID, Class<br>1,1<br></pre>",
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      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
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    }
  ]
}