DEVELOPMENT... { "data_id": "1040", "name": "sylva_prior", "exact_name": "sylva_prior", "version": 1, "version_label": null, "description": "**Author**: \r\n**Source**: Unknown - Date unknown \r\n**Please cite**: \r\n\r\nDatasets from the Agnostic Learning vs. Prior Knowledge Challenge (http:\/\/www.agnostic.inf.ethz.ch)\r\n\r\n**Note: Derived from the covertype dataset**\r\n\r\nDataset from: http:\/\/www.agnostic.inf.ethz.ch\/datasets.php\r\n\r\nModified by TunedIT (converted to ARFF format)\r\n\r\n\r\nSYLVA is the ecology database\r\n\r\n\r\nThe task of SYLVA is to classify forest cover types. The forest cover type for 30 x 30 meter cells is obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. We brought it back to a two-class classification problem (classifying Ponderosa pine vs. everything else). The \"agnostic learning track\" data consists in 216 input variables. Each pattern is composed of 4 records: 2 true records matching the target and 2 records picked at random. Thus 1\/2 of the features are distracters. The \"prior knowledge track\" data is identical to the \"agnostic learning track\" data, except that the distracters are removed and the identity of the features is revealed. For that track, the forest cover original ids are revealed for training data.\r\n\r\nData type: non-sparse\r\nNumber of features: 108\r\nNumber of examples and check-sums:\r\nPos_ex Neg_ex Tot_ex Check_sum\r\nTrain 805 12281 13086 118996108.00\r\nValid 81 1228 1309 11904801.00", "format": "ARFF", "uploader": "Felicia West", "uploader_id": 2, "visibility": "public", "creator": "IJCNN 2007 Workshop on Agnostic Learning vs. Prior Knowledge", "contributor": null, "date": "2014-10-06 23:56:07", "update_comment": null, "last_update": "2014-10-06 23:56:07", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/53923\/sylva_prior.arff", "default_target_attribute": "label", "row_id_attribute": null, "ignore_attribute": null, "runs": 486, "suggest": { "input": [ "sylva_prior", "Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http:\/\/www.agnostic.inf.ethz.ch) Dataset from: http:\/\/www.agnostic.inf.ethz.ch\/datasets.php Modified by TunedIT (converted to ARFF format) SYLVA is the ecology database The task of SYLVA is to classify forest cover types. The forest cover type for 30 x 30 meter cells is obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. We brought it back to a two-class classification problem (classify " ], "weight": 5 }, "qualities": { "NumberOfInstances": 14395, "NumberOfFeatures": 109, "NumberOfClasses": 2, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 108, "NumberOfSymbolicFeatures": 1, "AutoCorrelation": 0.8849520633597332, "CfsSubsetEval_DecisionStumpAUC": 0.9564474365137731, "CfsSubsetEval_DecisionStumpErrRate": 0.00916985064258423, "CfsSubsetEval_DecisionStumpKappa": 0.9212863872434351, "CfsSubsetEval_NaiveBayesAUC": 0.9564474365137731, "CfsSubsetEval_NaiveBayesErrRate": 0.00916985064258423, "CfsSubsetEval_NaiveBayesKappa": 0.9212863872434351, "CfsSubsetEval_kNN1NAUC": 0.9564474365137731, "CfsSubsetEval_kNN1NErrRate": 0.00916985064258423, "CfsSubsetEval_kNN1NKappa": 0.9212863872434351, "ClassEntropy": 0.3335640121480651, "DecisionStumpAUC": 0.9097667018075233, "DecisionStumpErrRate": 0.06154914901007294, "DecisionStumpKappa": 0, "Dimensionality": 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