{ "data_id": "350", "name": "webdata_wXa", "exact_name": "webdata_wXa", "version": 1, "version_label": null, "description": "**Author**: John Platt \r\n**Source**: [libSVM](http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvmtools\/datasets) - Date unknown \r\n**Please cite**: John C. Platt. \r\nFast training of support vector machines using sequential minimal optimization. \r\nIn Bernhard Sch\u00f6lkopf, Christopher J. C. Burges, and Alexander J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, 1998. MIT Press.a\r\n\r\nThis is the famous webdata dataset w[1-8]a in its binary version, retrieved 2014-11-14 from the libSVM site. Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows: \r\n\r\n* load all web data datasets, train and test, e.g. w1a, w1a.t, w2a, w2a.t, w3a, ... \r\n* join test and train for each subset, e.g. w1a and w1a.t, w2a and w2a.t \r\n* normalize each file columnwise according to the following rules: \r\n* If a column only contains one value (constant feature), it will set to zero and thus removed by sparsity. \r\n* If a column contains two values (binary feature), the value occuring more often will be set to zero, the other to one. \r\n* If a column contains more than two values (multinary\/real feature), the column is divided by its std deviation.\r\n* afterwards all these 8 files are merged into one, and randomly sorted. \r\n* duplicate lines were finally removed.\r\n\r\nAn R script which does all of these steps can be found here:\r\nhttps:\/\/github.com\/openml\/data_scripts\/blob\/master\/webdata_wXa\/dataDownloader.R", "format": "Sparse_ARFF", "uploader": "aydin demircioglu", "uploader_id": 167, "visibility": "public", "creator": "John Platt", "contributor": null, "date": "2014-08-29 18:29:14", "update_comment": null, "last_update": "2014-08-29 18:29:14", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/52253\/phpp41X7N", "default_target_attribute": "Y", "row_id_attribute": null, "ignore_attribute": null, "runs": 564, "suggest": { "input": [ "webdata_wXa", "Fast training of support vector machines using sequential minimal optimization. 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Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows: * load all web data datasets, " ], "weight": 5 }, "qualities": { "NumberOfInstances": 36974, "NumberOfFeatures": 124, "NumberOfClasses": 2, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 123, "NumberOfSymbolicFeatures": 1, "Quartile2SkewnessOfNumericAtts": 4.895902671456608, "REPTreeDepth3Kappa": 0.487586785315756, "DecisionStumpKappa": 0, "MaxMeansOfNumericAtts": 0.9425812733272083, "MinMutualInformation": null, "PercentageOfBinaryFeatures": 0.8064516129032258, "Quartile2StdDevOfNumericAtts": 0.18909315293680332, "RandomTreeDepth1AUC": 0.6808424988189737, "Dimensionality": 0.003353708011034781, "MaxMutualInformation": null, "MinNominalAttDistinctValues": 2, "PercentageOfInstancesWithMissingValues": 0, "Quartile3AttributeEntropy": null, "RandomTreeDepth1ErrRate": 0.24457727051441552, "EquivalentNumberOfAtts": null, 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