{
  "fetched_at": "2026-10-19T19:05:31Z",
  "source_url": "https://www.kaggle.com/competitions/ga-ms-ds-kaggle/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": "data-description",
      "content": "Train.csv is all y'all need :)",
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    {
      "name": "Description",
      "content": "In this competition you'll be predicting whether or not a Stack Overflow Question will be **answered (1)** or **not answered (0)**. This data set contains some text fields so feel free to leverage NLP to convert your text into features to use in your model!\n\nYou'll train with your train data set and evaluate with your test data set. Yes, you can split your training set further into another train and test set, but it is not necessary.",
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    {
      "name": "Evaluation",
      "content": "<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>Submission files should contain two columns: **id** and **y**. **id** corresponds to the ids of the predictions you'll make, **y** corresponds to the predictions you'll make.",
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