{
  "fetched_at": "2026-10-19T19:13:48Z",
  "source_url": "https://www.kaggle.com/competitions/2014-2-5k12gr-lab2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p><strong>File descriptions</strong></p>\r\n<p><strong>train.csv</strong> - the training set<br /><strong>test.csv</strong> - the test set<br /><br /></p>\r\n<p><strong>Attribute Information:</strong></p>\r\n<p>Given is the attribute name, attribute type, the measurement unit and a brief description. The forest cover type is the classification problem. The order of this listing corresponds to the order of numerals along the rows of the database.</p>\r\n<p>Name / Data Type / Measurement / Description</p>\r\n<p>Elevation / quantitative /meters / Elevation in meters <br />Aspect / quantitative / azimuth / Aspect in degrees azimuth <br />Slope / quantitative / degrees / Slope in degrees <br />Horizontal_Distance_To_Hydrology / quantitative / meters / Horz Dist to nearest surface water features <br />Vertical_Distance_To_Hydrology / quantitative / meters / Vert Dist to nearest surface water features <br />Horizontal_Distance_To_Roadways / quantitative / meters / Horz Dist to nearest roadway <br />Hillshade_9am / quantitative / 0 to 255 index / Hillshade index at 9am, summer solstice <br />Hillshade_Noon / quantitative / 0 to 255 index / Hillshade index at noon, summer soltice <br />Hillshade_3pm / quantitative / 0 to 255 index / Hillshade index at 3pm, summer solstice <br />Horizontal_Distance_To_Fire_Points / quantitative / meters / Horz Dist to nearest wildfire ignition points <br />Wilderness_Area (4 binary columns) / qualitative / 0 (absence) or 1 (presence) / Wilderness area designation <br />Soil_Type (40 binary columns) / qualitative / 0 (absence) or 1 (presence) / Soil Type designation <br />Cover_Type (7 types) / integer / 1 to 7 / Forest Cover Type designation</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "<p><strong>Data Set Information:</strong></p>\r\n<p>Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types).</p>\r\n<p>This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices.</p>\r\n<p>Some background information for these four wilderness areas: Neota (area 2) probably has the highest mean elevational value of the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3) would have a lower mean elevational value, while Cache la Poudre (area 4) would have the lowest mean elevational value.</p>\r\n<p>As for primary major tree species in these areas, Neota would have spruce/fir (type 1), while Rawah and Comanche Peak would probably have lodgepole pine (type 2) as their primary species, followed by spruce/fir and aspen (type 5). Cache la Poudre would tend to have Ponderosa pine (type 3), Douglas-fir (type 6), and cottonwood/willow (type 4).</p>\r\n<p>The Rawah and Comanche Peak areas would tend to be more typical of the overall dataset than either the Neota or Cache la Poudre, due to their assortment of tree species and range of predictive variable values (elevation, etc.) Cache la Poudre would probably be more unique than the others, due to its relatively low elevation range and species composition.</p>\r\n<p><br /><br /></p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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>\r\n<hr />\r\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>\r\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\r\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>\r\n<h2>Submission Format</h2>\r\n<p>Для каждой строки из тестового файла необходимо выставить метку класса.</p>\r\n<p>Файл должен содержать заголовок, и иметь следующий формат:</p>\r\n<pre>id,class<br /><span style=\"font-size: 14px; line-height: 1.4em;\">0,2<br />1,2<br />2,2<br />3,2<br />4,6<br />5,1<br />6,2<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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 15 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>10/21/2014 10:31 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>10/28/2014 11:59 PM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<h1></h1>\r\n<p>При решении задачи можно использовать все алгоритмы, пройденные к этому времени на лекциях: </p>\r\n<ul>\r\n<li><span style=\"font-size: 1em; line-height: 1.5em;\">k-Nearest Neighbors algorithm</span></li>\r\n<li><span style=\"font-size: 1em; line-height: 1.5em;\">SVM</span></li>\r\n<li><span style=\"font-size: 1em; line-height: 1.5em;\">Naive Bayes classifier</span></li>\r\n<li><span style=\"font-size: 1em; line-height: 1.5em;\">Decision tree</span></li>\r\n<li><span style=\"font-size: 1em; line-height: 1.5em;\">а так же любые эвристики, которые приходят на ум (при условиях полной самостоятельной реализации алгоритмов)</span></li>\r\n</ul>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}