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diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german.doc b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german.doc new file mode 100644 index 00000000..38b80dcb --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/german.doc @@ -0,0 +1,175 @@ +Description of the German credit dataset. + +1. Title: German Credit data + +2. Source Information + +Professor Dr. Hans Hofmann +Institut f"ur Statistik und "Okonometrie +Universit"at Hamburg +FB Wirtschaftswissenschaften +Von-Melle-Park 5 +2000 Hamburg 13 + +3. Number of Instances: 1000 + +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 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. + + +6. Number of Attributes german: 20 (7 numerical, 13 categorical) + Number of Attributes german.numer: 24 (24 numerical) + + +7. Attribute description for german + +Attribute 1: (qualitative) + Status of existing checking account + A11 : ... < 0 DM + A12 : 0 <= ... < 200 DM + A13 : ... >= 200 DM / + salary assignments for at least 1 year + A14 : no checking account + +Attribute 2: (numerical) + Duration in month + +Attribute 3: (qualitative) + Credit history + A30 : no credits taken/ + all credits paid back duly + A31 : all credits at this bank paid back duly + A32 : existing credits paid back duly till now + A33 : delay in paying off in the past + A34 : critical account/ + other credits existing (not at this bank) + +Attribute 4: (qualitative) + Purpose + A40 : car (new) + A41 : car (used) + A42 : furniture/equipment + A43 : radio/television + A44 : domestic appliances + A45 : repairs + A46 : education + A47 : (vacation - does not exist?) + A48 : retraining + A49 : business + A410 : others + +Attribute 5: (numerical) + Credit amount + +Attibute 6: (qualitative) + Savings account/bonds + A61 : ... < 100 DM + A62 : 100 <= ... < 500 DM + A63 : 500 <= ... < 1000 DM + A64 : .. >= 1000 DM + A65 : unknown/ no savings account + +Attribute 7: (qualitative) + Present employment since + A71 : unemployed + A72 : ... < 1 year + A73 : 1 <= ... < 4 years + A74 : 4 <= ... < 7 years + A75 : .. >= 7 years + +Attribute 8: (numerical) + Installment rate in percentage of disposable income + +Attribute 9: (qualitative) + Personal status and sex + A91 : male : divorced/separated + A92 : female : divorced/separated/married + A93 : male : single + A94 : male : married/widowed + A95 : female : single + +Attribute 10: (qualitative) + Other debtors / guarantors + A101 : none + A102 : co-applicant + A103 : guarantor + +Attribute 11: (numerical) + Present residence since + +Attribute 12: (qualitative) + Property + A121 : real estate + A122 : if not A121 : building society savings agreement/ + life insurance + A123 : if not A121/A122 : car or other, not in attribute 6 + A124 : unknown / no property + +Attribute 13: (numerical) + Age in years + +Attribute 14: (qualitative) + Other installment plans + A141 : bank + A142 : stores + A143 : none + +Attribute 15: (qualitative) + Housing + A151 : rent + A152 : own + A153 : for free + +Attribute 16: (numerical) + Number of existing credits at this bank + +Attribute 17: (qualitative) + Job + A171 : unemployed/ unskilled - non-resident + A172 : unskilled - resident + A173 : skilled employee / official + A174 : management/ self-employed/ + highly qualified employee/ officer + +Attribute 18: (numerical) + Number of people being liable to provide maintenance for + +Attribute 19: (qualitative) + Telephone + A191 : none + A192 : yes, registered under the customers name + +Attribute 20: (qualitative) + foreign worker + A201 : yes + A202 : no + + + +8. Cost Matrix + +This dataset requires use of a cost matrix (see below) + + + 1 2 +---------------------------- + 1 0 1 +----------------------- + 2 5 0 + +(1 = Good, 2 = Bad) + +the rows represent the actual classification and the columns +the predicted classification. + +It 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). + |