{
  "fetched_at": "2026-10-22T09:46:28Z",
  "source_url": "https://www.kaggle.com/competitions/cs589-f19-hw1/data",
  "pages": [
    {
      "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!",
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    {
      "name": "Description",
      "content": "Please use our other Kaggle competition link, https://www.kaggle.com/c/cs589-hw1-f19-finalversion, instead of this one!!",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions. d here:</p>\n<hr />\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, commonly used in information retrieval, 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.</p>\n<h2>Submission Format</h2>\n<p><strong>Kaggle submissions should have two columns. The first column, with header name \"Id\", is just a numerical count of the ID # in data prediction entries from 0 to n-1. The second column, with header name \"Predicted\", contains the prediction values for all the data points in the testing set.",
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