You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\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
\r\nTeam mergers are not allowed in this competition.
\r\n\r\nThere is no maximum team size.
\r\nYou may submit a maximum of 2 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: TBA
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 2/1/2014 12:00 AM UTC
\r\n\t\r\nThe 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.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "This page shows up underneath the data files. Here you describe what files you have provided and the format of each. There is no single format for this page that is appropriate for all competitons, but you should strive to describe as much as you can here. A little time spent describing the data hera\n
To format pages, stick to the following conventions:
\n<p>Paragraphs should go in p tags</p>\n
<pre>Code should go in pre tags</pre>\n
<h2>Subtitles should go in h2 tags</h2>\n
You can display equations using LaTeX enclosed in escaped brackets. For example, this:
\n\\[ \\epsilon = \\sqrt{\\frac{1}{n} \\sum_{i=1}^n (\\log(p_i + 1) - \\log(a_i+1))^2 } \\]
\nis created by this:
\n\\[\n\\epsilon = \\sqrt{\\frac{1}{n} \\sum_{i=1}^n (\\log(p_i + 1) - \\log(a_i+1))^2 }\n\\]\nWe thank Professor Plum, Ph.D. for providing this dataset.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The evaluation pages describes ho\n
The evaluation metric for this competition is Mean F1-Score. 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:
\n\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]
\nThe 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.
\nFor every author in the dataset, 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. 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).
\nThe file should contain a header and have the following format:
\nAuthorId,DuplicateAuthorIds", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }
1,1
8,8
9,9 10
10,10 9
etc.