{ "data_id": "41082", "name": "USPS", "exact_name": "USPS", "version": 2, "version_label": null, "description": "The dataset and this description is made available on\r\nhttp:\/\/www-stat.stanford.edu\/~tibs\/ElemStatLearn\/data.html.\r\n\r\nNormalized handwritten digits, automatically\r\nscanned from envelopes by the U.S. Postal Service. The original\r\nscanned digits are binary and of different sizes and orientations; the\r\nimages here have been deslanted and size normalized, resulting\r\nin 16 x 16 grayscale images (Le Cun et al., 1990).\r\n\r\nThe data are in two gzipped files, and each line consists of the digit\r\nid (0-9) followed by the 256 grayscale values.\r\n\r\n\r\nThere are 7291 training observations and 2007 test observations,\r\ndistributed as follows:\r\n0 1 2 3 4 5 6 7 8 9 Total\r\nTrain 1194 1005 731 658 652 556 664 645 542 644 7291\r\nTest 359 264 198 166 200 160 170 147 166 177 2007\r\n\r\nor as proportions:\r\n0 1 2 3 4 5 6 7 8 9 \r\nTrain 0.16 0.14 0.1 0.09 0.09 0.08 0.09 0.09 0.07 0.09\r\nTest 0.18 0.13 0.1 0.08 0.10 0.08 0.08 0.07 0.08 0.09\r\n\r\n\r\nAlternatively, the training data are available as separate files per\r\ndigit (and hence without the digit identifier in each row)\r\n\r\nThe test set is notoriously \"difficult\", and a 2.5% error rate is\r\nexcellent. These data were kindly made available by the neural network\r\ngroup at AT&T research labs (thanks to Yann Le Cunn).\r\n\r\nPS: In this dataset, the class is represented as 1-10", "format": "ARFF", "uploader": "Irfan Nur Afif", "uploader_id": 2506, "visibility": "public", "creator": null, "contributor": null, "date": "2018-05-14 17:07:23", "update_comment": null, "last_update": "2018-05-14 17:07:23", "licence": "CC0", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/19329737\/usps.arff", "default_target_attribute": "int0", "row_id_attribute": null, "ignore_attribute": null, "runs": 56, "suggest": { "input": [ "USPS", "The dataset and this description is made available on http:\/\/www-stat.stanford.edu\/~tibs\/ElemStatLearn\/data.html. Normalized handwritten digits, automatically scanned from envelopes by the U.S. Postal Service. The original scanned digits are binary and of different sizes and orientations; the images here have been deslanted and size normalized, resulting in 16 x 16 grayscale images (Le Cun et al., 1990). The data are in two gzipped files, and each line consists of the digit id (0-9) followed by " ], "weight": 5 }, "qualities": { "NumberOfInstances": 9298, "NumberOfFeatures": 257, "NumberOfClasses": 10, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 256, "NumberOfSymbolicFeatures": 1, "Quartile1SkewnessOfNumericAtts": 0.27881186145340053, "MeanMeansOfNumericAtts": -0.49771670513442073, "Quartile1StdDevOfNumericAtts": 0.3866860773963559, "AutoCorrelation": 0.12186726901150909, "MeanMutualInformation": null, "Quartile2AttributeEntropy": null, "ClassEntropy": 3.272802610125959, "MeanNoiseToSignalRatio": null, "Quartile2KurtosisOfNumericAtts": -0.6933252350001895, "Dimensionality": 0.027640352764035276, "MeanNominalAttDistinctValues": 10, "Quartile2MeansOfNumericAtts": -0.468290109109486, "EquivalentNumberOfAtts": null, "MeanSkewnessOfNumericAtts": 1.4656762135687167, "Quartile2MutualInformation": null, "MajorityClassPercentage": 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