germancredit: Modified german credit dataset
In fairness: Algorithmic Fairness Metrics

germancredit is a credit scoring data set that can be used to study algorithmic (un)fairness. This data was used to predict defaults on consumer loans in the German market. In this dataset, a model to predict default has already been fit and predicted probabilities and predicted status (yes/no) for default have been concatenated to the original data.

1	germancredit

A data frame with 1000 rows and 23 variables:

Account_status: factor, status of existing checking account
Duration: numeric, loan duration in month
Credit_history: factor, previous credit history
Purpose: factor, loan purpose
Amount: numeric, credit amount
Savings: factor, savings account/bonds
Employment: factor, present employment since
Installment_rate: numeric, installment rate in percentage of disposable income
Guarantors: factor, other debtors / guarantors
Resident_since: factor, present residence since
Property: factor, property
Age: numeric, age in years
Other_plans: factor, other installment plans
Housing: factor, housing
Num_credits: numeric, Number of existing credits at this bank
Job: factor, job
People_maintenance: numeric, number of people being liable to provide maintenance for
Phone: factor, telephone
Foreign: factor, foreign worker
BAD: factor, GOOD/BAD for whether a customer has defaulted on a loan. This is the outcome or target in this dataset
Female: factor, female/male for gender
probability: numeric, predicted probabilities for default, ranges from 0 to 1
predicted: numeric, predicted values for default, 0/1 for no/yes

The dataset has undergone modifications (e.g. categorical variables were encoded, prediction model was fit and predicted probabilities and predicted status were concatenated to the original dataset).

fairness documentation built on April 14, 2021, 5:09 p.m.