| .names file created by George John, October 1994 | Statlog Processing: atts converted to numbers | Ronny Kohavi: converted last 12 attributes to 0,1 because they're binary. | These gives large speed improvements in C4.5 | |1. TITLE: | German Credit data | |2. USE IN STATLOG | | 2.1- Testing Mode | 10-Fold Cross Validation | | 2.2- Special Preprocessing | Yes | | 2.3- Test Results | Cost TIME | Algorithm Train Test Train Test | -------------------------------------------- | Discrim 0.509 0.535 50 7 | LogDisc 0.499 0.538 56 7 | Castle 1.276 0.583 182 12 | Alloc80 0.597 0.584 912 ? | Dipol92 ? 0.599 | Smart 0.389 0.601 489 ? | Cal 50.637 0.603 65 ? | Cart 0.581 0.613 114 1 | QuaDisc ? 0.619 | KNN 0 0.694 2 9 | Default ? 0.700 | Bayes 0.6 0.703 5 1 | IndCart ? 0.761 | BackProp ? 0.772 | BayTree ? 0.778 | Cn2 0 0.856 117 3 | Ac2 0 0.878 1701 41 | Itrule ? 0.879 | NewId ? 0.925 | LVQ ? 0.963 | Radial ? 0.971 | C4.5 0.64 0.985 14 1 | Kohonen ? 1.160 | Cascade ? 100.00 | |3. SOURCES and PAST USAGE | 3.1 SOURCES | Professor Dr. Hans Hofmann | Institut f"ur Statistik und "Okonometrie | Universit"at Hamburg | FB Wirtschaftswissenschaften | Von-Melle-Park 5 | 2000 Hamburg 13 | | Two datasets are provided. the original dataset, in the form provided | by Prof. Hofmann, contains categorical/symbolic attributes and | is in the file "german.dat". | | For algorithms that need numerical attributes, Strathclyde University | produced the file "german.numer". 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. | | |4. DATASET DISCRIPTION | NUMBER OF EXAMPLES: | 1000 | | NUMBER of CLASSES | 2 | Class 1 700 | Class 2 300 | | NUMBER OF ATTRIBUTES: | for german.dat: 20 (7 numerical, 13 categorical) | for german.numer: 24 (24 numerical) | | Attribute description for german.dat | | 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 | | | | Cost MATRIX: | | ! 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). | |CONTACTS | statlog-adm@ncc.up.pt | bob@stams.strathclyde.ac.uk | |================================================================================ | good,bad. Status: 0DM, less-200DM, over-200DM, no-account. Duration: continuous. Credit-history: all-paid-duly,bank-paid-duly,duly-till-now,delay,critical. Purpose: new-car,used-car,furniture,radio-tv,domestic-app,repairs,education,vacation, retraining,business,others. Credit: continuous. Savings-account: less100DM,less500DM,less1000DM,over1000DM,unknown. Employment: unemployed,one-year,four-years,seven-years,over-seven. Installment-rate: continuous. Personal-status: male-divorced,female-divorced,single-male,married-male,single-female. Debtors: none,co-applicant,guarantor. Residence-time: continuous. Property: real-estate,building-society,car,none. Age: continuous. Installments: bank,stores,none. Housing: rent,own,free. Existing-credits: continuous. Job: unemployed-non-resident,unskilled-resident,skilled,management. Liable-people: continuous. Telephone: yes,no. Foreign: yes,no.