{
  "fetched_at": "2026-10-19T22:01:06Z",
  "source_url": "https://www.kaggle.com/competitions/enpm808w-fall-2020-hw3-question-answering/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Each line has a guess (page) and a correct answer (answer) given some fraction of the question revealed so far (text). Your goal is to predict whether they match. Each guess is the title of a Wikipedia page. To get you started, you have the following columns\n\nrow A unique ID for every guess\n\nbody_score A measure of the confidence for how well the text of the Wikipedia page matches the question text\n\npage The guess produced\n\nanswer The correct answer\n\ntext The text so far revealed (randomly chosen)\n\ncategory The category (could be noisy) of the question\n\ntournaments The tournaments the question was used in\n\nanswer_type The kind of answer of the question (e.g. person, place, work)\n\ncorr Whether the guess (page) was correct or not (i.e., whether it matched “answer”)\n\ninlinks The number of inlinks on the guessed Wikipedia page\n\nFile descriptions\n\nqb.train.csv - the training set\n\nqb.test.csv - the test set\n\nqb.guess.csv - a sample submission file in the correct format\n\nfeatures.R - R code to generate some additional features (described in the lecture)\n\n",
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      "postTitle": "",
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    {
      "name": "Description",
      "content": "The evaluation format is accuracy. Because there are more wrong answers than correct answers, you should always get above 0.53 accuracy. This is the simplest baseline you should do better than.\n\nSubmission Format\n\nFor row in the test file, submission files should contain two columns: row and corr. You should provide for each row whether the guess is correct or not.\n\nThe file should contain a header and have the following format:\n\nrow,corr\n31,False\n32,False\n33,False\n88,False\n91,False\n139,False\n140,False\n141,False\n142,False\n188,True",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied 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>For every author in the dataset</strong>, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids.&nbsp;For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br>1,1<br></pre>",
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      "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!",
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