{
  "fetched_at": "2026-10-20T12:27:37Z",
  "source_url": "https://www.kaggle.com/competitions/decisiontrees/data",
  "pages": [
    {
      "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": "<h2>File descriptions</h2>\n<ul>\n<li><strong>training.csv</strong> - the training set</li>\n<li><strong>testing.csv</strong> - the test set</li>\n<li><strong>sample.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>1. Clump Thickness 1 - 10</strong> </li>\n<li><strong>2. Uniformity of Cell Size 1 - 10</strong> </li>\n<li><strong>3. Uniformity of Cell Shape</strong> </li>\n<li><strong>4. Marginal Adhesion 1 - 10</strong> </li>\n<li><strong>5. Single Epithelial Cell SIze 1 - 10</strong> </li>\n<li><strong>6. Bare Nuclei 1 - 10</strong> </li>\n<li><strong>7. Bland Chromatin 1 - 10</strong> </li>\n<li><strong>8. Normal Nucleoli 1 - 10</strong> </li>\n<li><strong>9. Mitoses 1 - 10</strong> </li>\n<li><strong>10. Class (0 for benign, 1 for malignant)</strong> </li>\n</ul>\n<h2>More info</h2>\n<ul>\n<p>Your submission should be your predictions of the testing data in order. Each line should have the id (line number, i.e. 1, 2, 3, ...) and the class (0 or 1). Look at the sample submission for clarification.</p>\n</ul>\n",
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    },
    {
      "name": "Description",
      "content": "<p>Welcome to TJ Machine Learning Club's first contest of the year!<\\p>\n\n<h1><p><strong>Important Rules for This Contest</strong></p></h1>\n<p>You are not allowed to use a library (other than numpy).</p>\n\n<p>Submissions close Tuesday, 10/9, at 11:59:00 P.M.</p>\n\n<p>The leaderboard calculates a random 50% of the testing data, and ranks users publicly based on only that 50% of the data. Once the competition ends, a private leaderboard will be shown with the results for the other 50% of the testing data. The private leaderboard is what counts for the final rankings.</p>\n\n<p>Why do it this way? This is to prevent people from simply submitting over and over again, seeing their leaderboard score improve by a little, and keep guessing or making their machine learning algorithm overfit the public data to artifically reach a higher score. We want the solution that best generalizes to unseen future data, which is why we have the private leaderboard system.</p>",
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    },
    {
      "name": "Evaluation",
      "content": "<p>All submissions are evaluated using the <a href=\"https://en.wikipedia.org/wiki/Receiver_operating_characteristic\">area under the ROC score</a>.</p>",
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