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  "fetched_at": "2026-10-25T01:02:03Z",
  "source_url": "https://www.kaggle.com/competitions/cosi-143b-brandeis/data",
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      "name": "data-description",
      "content": "#Data Set Information\n\nThis dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981).  The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like \"leaflets three, let it be'' for Poisonous Oak and Ivy.\n\nYou can find more information about this data set [here](https://archive.ics.uci.edu/ml/datasets/mushroom) ",
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      "name": "Description",
      "content": "In this assignment you will train a binary classifier for identifying poisonous mushrooms. You will be provided a data set with mushroom records. Each mushroom record is labeled as (1) definitely edible (2) definitely poisonous and (3) of unknown edibility (in which case it is not recommended for eating). In your data set there are actually only two classes: (1) definitely edible and (2) unknown (the unknown edibility class class was combined with the poisonous one.)\n\nYour task is to train and build a classifier that predicts the class of a mushroom. You may use an classification method we discussed in class \n\n#Inspiration\nWhat types of machine learning models perform best on this dataset?\n\nWhich features are most indicative of a poisonous mushroom?\n\n#Submission \n\nYour submission should have 2467 rows, each with 2 columns. The first columns is the id of the mushroom record and the second one the predicted value ('e' (for edible or 'p' for 'poisonous /possibly poisonous')\n\n#Acknowledgements\nThis dataset was originally donated to the UCI Machine Learning repository. You can learn more about past research using the data here.\n\n",
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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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    {
      "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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