{ "data_id": "40771", "name": "GermanCredit-train", "exact_name": "GermanCredit-train", "version": 1, "version_label": null, "description": "Source:\r\n\r\nProfessor Dr. Hans Hofmann \r\nInstitut f"ur Statistik und "Okonometrie \r\nUniversit"at Hamburg \r\nFB Wirtschaftswissenschaften \r\nVon-Melle-Park 5 \r\n2000 Hamburg 13 \r\n\r\n\r\nData Set Information:\r\n\r\nTwo datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical\/symbolic attributes and is in the file "german.data". \r\n\r\nFor algorithms that need numerical attributes, Strathclyde University produced the file "german.data-numeric". This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This was the form used by StatLog. \r\n\r\nThis dataset requires use of a cost matrix (see below) \r\n\r\n..... 1 2 \r\n---------------------------- \r\n1 0 1 \r\n----------------------- \r\n2 5 0 \r\n\r\n(1 = Good, 2 = Bad) \r\n\r\nThe rows represent the actual classification and the columns the predicted classification. \r\n\r\nIt is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1). \r\n\r\n\r\nAttribute Information:\r\n\r\nAttribute 1: (qualitative) \r\nStatus of existing checking account \r\nA11 : ... < 0 xss=removed>= 200 DM \/ salary assignments for at least 1 year \r\nA14 : no checking account \r\n\r\nAttribute 2: (numerical) \r\nDuration in month \r\n\r\nAttribute 3: (qualitative) \r\nCredit history \r\nA30 : no credits taken\/ all credits paid back duly \r\nA31 : all credits at this bank paid back duly \r\nA32 : existing credits paid back duly till now \r\nA33 : delay in paying off in the past \r\nA34 : critical account\/ other credits existing (not at this bank) \r\n\r\nAttribute 4: (qualitative) \r\nPurpose \r\nA40 : car (new) \r\nA41 : car (used) \r\nA42 : furniture\/equipment \r\nA43 : radio\/television \r\nA44 : domestic appliances \r\nA45 : repairs \r\nA46 : education \r\nA47 : (vacation - does not exist?) \r\nA48 : retraining \r\nA49 : business \r\nA410 : others \r\n\r\nAttribute 5: (numerical) \r\nCredit amount \r\n\r\nAttibute 6: (qualitative) \r\nSavings account\/bonds \r\nA61 : ... < 100 xss=removed xss=removed>= 1000 DM \r\nA65 : unknown\/ no savings account \r\n\r\nAttribute 7: (qualitative) \r\nPresent employment since \r\nA71 : unemployed \r\nA72 : ... < 1 xss=removed xss=removed>= 7 years \r\n\r\nAttribute 8: (numerical) \r\nInstallment rate in percentage of disposable income \r\n\r\nAttribute 9: (qualitative) \r\nPersonal status and sex \r\nA91 : male : divorced\/separated \r\nA92 : female : divorced\/separated\/married \r\nA93 : male : single \r\nA94 : male : married\/widowed \r\nA95 : female : single \r\n\r\nAttribute 10: (qualitative) \r\nOther debtors \/ guarantors \r\nA101 : none \r\nA102 : co-applicant \r\nA103 : guarantor \r\n\r\nAttribute 11: (numerical) \r\nPresent residence since \r\n\r\nAttribute 12: (qualitative) \r\nProperty \r\nA121 : real estate \r\nA122 : if not A121 : building society savings agreement\/ life insurance \r\nA123 : if not A121\/A122 : car or other, not in attribute 6 \r\nA124 : unknown \/ no property \r\n\r\nAttribute 13: (numerical) \r\nAge in years \r\n\r\nAttribute 14: (qualitative) \r\nOther installment plans \r\nA141 : bank \r\nA142 : stores \r\nA143 : none \r\n\r\nAttribute 15: (qualitative) \r\nHousing \r\nA151 : rent \r\nA152 : own \r\nA153 : for free \r\n\r\nAttribute 16: (numerical) \r\nNumber of existing credits at this bank \r\n\r\nAttribute 17: (qualitative) \r\nJob \r\nA171 : unemployed\/ unskilled - non-resident \r\nA172 : unskilled - resident \r\nA173 : skilled employee \/ official \r\nA174 : management\/ self-employed\/ \r\nhighly qualified employee\/ officer \r\n\r\nAttribute 18: (numerical) \r\nNumber of people being liable to provide maintenance for \r\n\r\nAttribute 19: (qualitative) \r\nTelephone \r\nA191 : none \r\nA192 : yes, registered under the customers name \r\n\r\nAttribute 20: (qualitative) \r\nforeign worker \r\nA201 : yes \r\nA202 : no \r\n\r\n#autoxgboost #autoweka", "format": "ARFF", "uploader": "Stefan Coors", "uploader_id": 1184, "visibility": "public", "creator": null, "contributor": null, "date": "2017-06-20 11:26:14", "update_comment": null, "last_update": "2017-06-20 11:26:14", "licence": "Public", "status": "in_preparation", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/5797183\/phpRUHuBA", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "GermanCredit-train", "Source: Professor Dr. Hans Hofmann Institut f"ur Statistik und "Okonometrie Universit"at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13 Data Set Information: Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical\/symbolic attributes and is in the file "german.data". For algorithms that need numerical attributes, Strathclyde University produced the file "german.data-numeric". This file " ], "weight": 5 }, "qualities": { "NumberOfInstances": 700, "NumberOfFeatures": 21, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 7, "NumberOfSymbolicFeatures": 14, "ClassEntropy": null, "MeanNoiseToSignalRatio": null, "Quartile2AttributeEntropy": null, "Dimensionality": 0.03, "MeanNominalAttDistinctValues": 4, "Quartile2KurtosisOfNumericAtts": 0.8757048072954641, "EquivalentNumberOfAtts": null, "MeanSkewnessOfNumericAtts": 0.8759809739797427, "Quartile2MeansOfNumericAtts": 2.9928571428571424, "MajorityClassPercentage": null, "MeanStdDevOfNumericAtts": 368.2614180285664, "Quartile2MutualInformation": null, "MajorityClassSize": null, "MinAttributeEntropy": null, "Quartile2SkewnessOfNumericAtts": 1.0737191826595598, "MaxAttributeEntropy": null, "MinKurtosisOfNumericAtts": -1.367374840606042, "PercentageOfBinaryFeatures": 14.285714285714285, "Quartile2StdDevOfNumericAtts": 1.1111119626674275, "MaxKurtosisOfNumericAtts": 3.875025803651701, "MinMeansOfNumericAtts": 1.16, "PercentageOfInstancesWithMissingValues": 0, "Quartile3AttributeEntropy": null, "MaxMeansOfNumericAtts": 3108.855714285714, "MinMutualInformation": null, "PercentageOfMissingValues": 0, "Quartile3KurtosisOfNumericAtts": 1.4594391289257715, "MaxMutualInformation": null, "MinNominalAttDistinctValues": 2, "PercentageOfNumericFeatures": 33.33333333333333, "Quartile3MeansOfNumericAtts": 35.47, "MaxNominalAttDistinctValues": 10, "MinSkewnessOfNumericAtts": -0.5632274996543148, "PercentageOfSymbolicFeatures": 66.66666666666666, "Quartile3MutualInformation": null, "MaxSkewnessOfNumericAtts": 1.8588376639116038, "MinStdDevOfNumericAtts": 0.3668681979650326, "Quartile1AttributeEntropy": null, "Quartile3SkewnessOfNumericAtts": 1.8580120326006688, "MaxStdDevOfNumericAtts": 2551.9057549237746, "MinorityClassPercentage": null, "Quartile1KurtosisOfNumericAtts": -1.1655458835164123, "Quartile3StdDevOfNumericAtts": 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