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  "fetched_at": "2026-10-20T02:14:39Z",
  "source_url": "https://www.kaggle.com/competitions/digging-into-data-spring-2018-hw3-qa/data",
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    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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    {
      "name": "data-description",
      "content": "See Description.",
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      "name": "Description",
      "content": "The goal of this homework is to predict whether the answer to a trivia question is correct or not. Thus, it is a binary classification problem. You will have to train effective classification models, but the more important (and effective) route for success is to engineer features and gather additional data to help your predictions.\n\nIt’s very easy to generate guesses (in fact, we could generate every possible guess). The challenge is knowing whether any given guess is good or not. We can treat this as a classification problem. Every guess can be described by features that measure how well it matches the question. The classifier tells us whether we got the question wrong or right.\n\nEach 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\n**row** A unique ID for every guess\n\n**body_score** A measure of the confidence for how well the text of the Wikipedia page matches the question text\n\n**page** The guess produced\n\n**answer** The correct answer\n\n**text** The text so far revealed (randomly chosen)\n\n**category** The category (could be noisy) of the question\n\n**tournaments** The tournaments the question was used in \n\n**answer_type** The kind of answer of the question (e.g. person, place, work)\n\n**corr** Whether the guess (page) was correct or not (i.e., whether it matched “answer”)\n\n**inlinks** 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)",
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    {
      "name": "Evaluation",
      "content": "Categorization accuracy is the evaluation used.",
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