{
  "fetched_at": "2026-10-20T05:22:15Z",
  "source_url": "https://www.kaggle.com/competitions/ds1718l3s/data",
  "pages": [
    {
      "name": "rules",
      "content": "\n\t<!-- Begin Base Rules -->\n    <h3>One account per participant</h3>\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n    <h3>No private sharing outside teams</h3>\n    <p>\n        Privately sharing code or data outside of teams is not permitted.\n        It's okay to share code if made available to all participants on the forums.\n    </p>\n    <h3>Team Mergers</h3>\n    <p>Team mergers are not allowed in this competition.</p>\n\n    <h3>Team Limits</h3>\n    <p>There is no maximum team size.</p>\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 30 entries per day.</p>\n    <p>You may select up to 1 final submissions for judging.</p>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>TBA</strong></p>\n    <p>Merger Deadline: <strong>None</strong></p>\n    <p>Entry Deadline: <strong>None</strong></p>\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\n\t<!-- End Base Rules -->\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": "<p>In this task, you are given a set of text messages, your goal is to identify which are spam messages. There is no pre-defined criteria of what is a spam message, you have to explore in the training dataset based on the given label.</p>\n<p>This task uses a real, public and non-encoded SMS spam collection [1] that is publicly available at: [https://archive.ics.uci.edu/ml/datasets/sms+spam+collection][1]. </p>\n\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.tsv</strong> - the training set, contains 3000 rows and 2 columns (''label'' and ''content''), <strong>tab</strong> delimited </li>\n<li><strong>test.tsv</strong> - the test set, contains 2574 rows and 2 columns (''id'' and ''content''), <strong>tab</strong> delimited</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format, contains 2574 rows and 2 columns (''id'' and ''label''), <strong>comma</strong> delimited</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>id</strong> - an id unique to a given test case, i.e. a text message</li>\n<li><strong>label</strong> - the label of a message indicates whether or not it is a spam message (1 - a spam message, 0 - not a spam message)</li>\n<li><strong>content</strong> - the content of a text message</li>\n</ul>\n\n<p>[1] Almeida, Tiago A., José María G. Hidalgo, and Akebo Yamakami. \"Contributions to the study of SMS spam filtering: new collection and results.\" In Proceedings of the 11th ACM symposium on Document engineering, pp. 259-262. ACM, 2011.</p>\n  [1]: https://archive.ics.uci.edu/ml/datasets/sms+spam+collection",
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    {
      "name": "Description",
      "content": "\n<p>In this task, you are given a set of text messages, your goal is to identify which are spam messages. </p>\n\n<p>For more details about the dataset please go to the Data page.</p>\n\n<p>For more details about the evaluation criteria please go to the Evaluation tab in this page.</p>",
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    {
      "name": "Evaluation",
      "content": "<hr />\n<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/F1_score\"> F1-Score</a> of the positive (spam) class. 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: id and label, comma delimited. </p>\n<p>The file should contain a header and have the following format:</p>\n<pre>id,label<br />1,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">8,0<br />9,0<br /></span></pre>",
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