{
  "fetched_at": "2026-10-20T10:19:40Z",
  "source_url": "https://www.kaggle.com/competitions/fast2/data",
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    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\r\n<ul>\r\n<li><strong>trainFile.mat: </strong>Matlab file containing training data and target values</li>\r\n<li><strong>testFile.mat:</strong> Matlab file containing only the test data</li>\r\n<li><strong>sample.csv:</strong> Sample submission file for predictions on test set</li>\r\n</ul>\r\n<p>This data is taken from UCI's machine learning repository on forest fires:</p>\r\n<p>https://archive.ics.uci.edu/ml/datasets/Forest+Fires</p>",
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      "name": "Description",
      "content": "<p><strong>Assignment 6</strong></p>\r\n<p><strong>FAST, National University of Computer &amp; Emerging Sciences, Lahore Campus</strong></p>\r\n<p>Build a regression model for predicting the area covered by a forest fire.  More details of the dataset are at:</p>\r\n<p>https://archive.ics.uci.edu/ml/datasets/Forest+Fires</p>\r\n<p>You have to apply regression with polynomial degree 1, 2 and 3.  For convenience, you can leave out the product terms between features.  You also have to apply regularization to all the three models with at least 3 values of lambda (the regularization constant).</p>\r\n<p>This assignment is also placed at:</p>\r\n<p>https://sites.google.com/site/sprfast2015/home/assignments</p>\r\n<p><strong>References</strong></p>\r\n<p>[Cortez and Morais, 2007] P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimarães, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9. Available at: https://archive.ics.uci.edu/ml/datasets/Forest+Fires</p>",
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    {
      "name": "Evaluation",
      "content": "<h2>Evaluation Measure</h2>\r\n<p>The evaluation is based upon the correlation coefficient between the predictions and the target values.  It has a value in [-1,1].  You are targeting a value close to one which means the predictions and target are highly linearly correlated.  A value close to zero indicates bad predictions and a correlation coefficient close to -1 means you should change the sign of predictions.</p>\r\n<h2>Submission Format</h2>\r\n<p>The sample.csv contains a sample submission file.  It is comma separated file, with first column being the id of the test set instance an the second one is your prediction on that instance.</p>\r\n<pre>Id,Value<br />1,1<br /><span style=\"font-size: 14px; line-height: 1.4em;\">2,8<br />3,9.7 <br /></span><span style=\"font-size: 14px; line-height: 1.4em;\"></span></pre>",
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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>The maximum size of a team is 1 participant.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 100 entries per day.</p>\r\n    <p>You may select up to 5 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>11/11/2015 3:40 PM UTC</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>11/29/2015 11:59 PM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n",
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