{ "data_id": "46", "name": "gina_agnostic", "exact_name": "gina_agnostic", "version": 1, "version_label": null, "description": "**Author**: \n**Source**: Unknown - Date unknown \n**Please cite**: \n\nDatasets from the Agnostic Learning vs. Prior Knowledge Challenge (http:\/\/www.agnostic.inf.ethz.ch)\n\nDataset from: http:\/\/www.agnostic.inf.ethz.ch\/datasets.php\n\n\nModified by TunedIT (converted to ARFF format)\n\n\nGINA is digit recognition database\n\nThe task of GINA is handwritten digit recognition. For the \"agnostic learning track\" we chose the problem of separating two-digit odd numbers from two-digit even numbers. Only the unit digit is informative for that task, therefore at least 1\/2 of the features are distracters. Additionally, the pixels that are almost always blank were removed and the pixel order was randomized to hide the feature identity. This is a two class classification problem with sparse continuous input variables, in which each class is composed of several clusters. It is a problem with heterogeneous classes.\n\nData type: non-sparse\nNumber of features: 970\nNumber of examples and check-sums:\nPos_ex\tNeg_ex\tTot_ex\tCheck_sum\nTrain\t 1550\t 1603\t 3153\t164947945.00\nValid\t 155\t 160\t 315\t16688946.00\n\n\nThis dataset contains samples from both training and validation datasets.", "format": "ARFF", "uploader": "Joaquin Vanschoren", "uploader_id": 2, "visibility": "public", "creator": null, "contributor": null, "date": "2014-10-06 23:56:01", "update_comment": null, "last_update": "2014-10-06 23:56:01", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/53921\/gina_agnostic.arff", "default_target_attribute": "label", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "gina_agnostic", "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) GINA is digit recognition database The task of GINA is handwritten digit recognition. For the \"agnostic learning track\" we chose the problem of separating two-digit odd numbers from two-digit even numbers. Only the unit digit is informative for that task, therefore at least 1\/2 of the features " ], "weight": 5 }, "qualities": { "NumberOfInstances": 3468, "NumberOfFeatures": 971, "NumberOfClasses": 2, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 970, "NumberOfSymbolicFeatures": 1, "AutoCorrelation": 0.500144216902221, "CfsSubsetEval_DecisionStumpAUC": 0.8611461069258446, "CfsSubsetEval_DecisionStumpErrRate": 0.1502306805074971, "CfsSubsetEval_DecisionStumpKappa": 0.6994458764772968, "CfsSubsetEval_NaiveBayesAUC": 0.8611461069258446, "CfsSubsetEval_NaiveBayesErrRate": 0.1502306805074971, "CfsSubsetEval_NaiveBayesKappa": 0.6994458764772968, "CfsSubsetEval_kNN1NAUC": 0.8611461069258446, "CfsSubsetEval_kNN1NErrRate": 0.1502306805074971, "CfsSubsetEval_kNN1NKappa": 0.6994458764772968, "ClassEntropy": 0.9997982272095376, "DecisionStumpAUC": 0.7249898949238418, "DecisionStumpErrRate": 0.2773933102652826, "DecisionStumpKappa": 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