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    "description": "**Author**: [Isabelle Guyon](isabelle@clopinet.com)  \r\n**Source**: [Agnostic Learning vs. Prior Knowledge Challenge](http:\/\/www.agnostic.inf.ethz.ch)  \r\n**Please cite**: None\r\n\r\n\r\nDataset from the Agnostic Learning vs. Prior Knowledge Challenge (http:\/\/www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLVA, GINA, NOVA, HIVA, ADA). The purpose of the challenge was to check if the performance of domain-specific feature engineering (prior knowledge) can be met by algorithms that were trained on data without any domain-specific knowledge (agnostic). For the latter, the data was anonymised and preprocessed in a way that makes them uninterpretable. \r\n\r\nThis dataset contains the agnostic (smashed) version of a data set from the Remote Sensing and GIS Program of Colorado State University for the time span June 2005 - September 2006. A Similar, raw and not-agnostic data set is termed __Covertype Dataset__ and can be found in the [UCI Database](https:\/\/archive.ics.uci.edu\/ml\/datasets\/covertype).\r\n\r\nModified by TunedIT (converted to ARFF format)\r\n\r\n\r\n\r\n### Topic\r\n\r\nThe task of SYLVA is to classify forest cover types. The forest cover type for 30 x 30\r\nmeter cells is obtained from US Forest Service (USFS) Region 2 Resource Information\r\nSystem (RIS) data. We brought it back to a two-class classification problem (classifying\r\nPonderosa pine vs. everything else). The \u201cagnostic data\u201d consists in 216 input variables.\r\nEach pattern is composed of 4 records: 2 true records matching the target and 2 records\r\npicked at random. Thus \u00bd of the features are distracters. The \u201cprior knowledge data\u201d is\r\nidentical to the \u201cagnostic data\u201d, except that the distracters are removed and the identity of\r\nthe features is revealed.\r\n\r\n### Description \r\n\r\nData type: non-sparse\r\nNumber of features: 216\r\nNumber of examples and check-sums:\r\nPos_ex Neg_ex Tot_ex Check_sum\r\nTrain   805 12281 13086 238271607.00\r\nValid    81  1228  1309 23817234.00\r\n\r\n\r\nThis dataset contains samples from both training and validation datasets.\r\n\r\n### Source\r\n\r\nOriginal owners: \r\nRemote Sensing and GIS Program\r\nDepartment of Forest Sciences\r\nCollege of Natural Resources\r\nColorado State University\r\nFort Collins, CO 80523\r\n(contact Jock A. Blackard, jblackard\/wo_ftcol@fs.fed.us\r\nor Dr. Denis J. Dean, denis@cnr.colostate.edu)\r\nJock A. Blackard\r\nUSDA Forest Service\r\n3825 E. Mulberry\r\nFort Collins, CO 80524 USA\r\njblackard\/wo_ftcol@fs.fed.us\r\n",
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