6.3.5. 2011 Kaggle competition Give Me Some Credit

6.3.5.1. Summary

This is a Kaggle dataset from the credit agency Credit Fusion [1]. The goal is to predict whether a customer will default on a loan in the next two years.

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted.

The dataset is fetched remotely from OpenML and cached locally on first use.

Classes	2
Defaulters	7616
Non-defaulters	105299
Samples	112915
Features	10

6.3.5.2. Using the Dataset

The dataset can be fetched through the fetch_give_me_some_credit function. This returns a Dataset object with the following attributes:

• data: the feature matrix

• target: the target vector

• cost_matrix: a CostMatrix with default values pre-filled

• instance_costs: a dict of per-instance cost drivers ('cl', 'fp_cost')

• feature_names: the feature names

• target_names: the target names

• DESCR: the full description of the dataset

import pandas as pd
from empulse.datasets import fetch_give_me_some_credit

dataset = fetch_give_me_some_credit(backend=pd)

The backend argument selects the dataframe library used for data and target. Pass the module itself — backend=pd for pandas or backend=pl for polars.

The following code snippet demonstrates how to load the dataset and fit a model using the CSLogitClassifier:

import pandas as pd
from empulse.datasets import fetch_give_me_some_credit
from empulse.models import CSLogitClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

dataset = fetch_give_me_some_credit(backend=pd)
X, y = dataset.data, dataset.target
cl = dataset.instance_costs['cl']
fp_cost = dataset.instance_costs['fp_cost']
fn_cost = cl * 0.75  # loss_given_default = 0.75

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', CSLogitClassifier())
])
pipeline.fit(
    X,
    y,
    model__fp_cost=fp_cost,
    model__fn_cost=fn_cost,
)

6.3.5.3. Cost Matrix

	Actual positive \(y_i = 1\)	Actual negative \(y_i = 0\)
Predicted positive \(\hat{y}_i = 1\)	tp_cost \(= 0\)	fp_cost \(= r_i + -\bar{r} \cdot \pi_0 + \bar{Cl} \cdot L_{gd} \cdot \pi_1\)
Predicted negative \(\hat{y}_i = 0\)	fn_cost \(= Cl_i \cdot L_{gd}\)	tn_cost \(= 0\)

with

• \(r_i\) : loss in profit by rejecting what would have been a good loan

• \(\bar{r}\) : average loss in profit by rejecting what would have been a good loan

• \(\pi_0\) : percentage of defaulters

• \(\pi_1\) : percentage of non-defaulters

• \(Cl_i\) : credit line of the client

• \(\bar{Cl}\) : average credit line

• \(L_{gd}\) : the fraction of the loan amount which is lost if the client defaults

Using default parameters, it is assumed that the interest rate is 4.79%, the cost of running the fund is 2.94%, the maximum credit line is 25,000, the loss given default is 75%, the term length is 24 months, and the loan to income ratio is 3. The default parameters are based on [2].

The interest rate, fund cost, maximum credit line, term length and loan-to-income ratio are applied when the dataset is built, and are baked into the 'fp_cost' and 'cl' arrays returned in instance_costs.

The loss given default remains symbolic, so it can be overridden at evaluation time by passing its alias loss_given_default to the metric:

import numpy as np
import pandas as pd
from empulse.datasets import fetch_give_me_some_credit
from empulse.metrics import Metric, Cost

dataset = fetch_give_me_some_credit(backend=pd)

# replace with your own model's predicted probabilities
y_score = np.random.default_rng(0).uniform(size=len(dataset.target))

cost = Metric(dataset.cost_matrix, Cost())
default_lgd = cost(dataset.target, y_score, **dataset.instance_costs)
higher_lgd = cost(dataset.target, y_score, loss_given_default=0.9, **dataset.instance_costs)

6.3.5.4. Data Description

Variable Name	Description	Type
monthly_income	Monthly income of borrower	numeric
debt_ratio	Monthly debt payments, alimony, living costs divided by monthly gross income	numeric
revolving_utilization	Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits	numeric
age	Age of borrower in years	numeric
n_dependents	Number of dependents in family excluding themselves (spouse, children etc.)	numeric
n_open_credit_lines	Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards)	numeric
n_real_estate_loans	Number of mortgage and real estate loans including home equity lines of credit	numeric
n_times_late_30_59_days	Number of times borrower has been 30-59 days past due but no worse in the last 2 years.	numeric
n_times_late_60_89_days	Number of times borrower has been 60-89 days past due but no worse in the last 2 years.	numeric
n_times_late_over_90_days	Number of times borrower has been 90 days or more past due.	numeric
default	Whether a person experienced 90 days past due delinquency or worse (‘yes’ = 1, ‘no’ = 0)	binary

6.3.5.5. References

[1]

Credit Fusion and Will Cukierski. Give Me Some Credit. https://kaggle.com/competitions/GiveMeSomeCredit, 2011. Kaggle.

[2]

A. Correa Bahnsen, D.Aouada, B, Ottersten, “Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring”, in Proceedings of the International Conference on Machine Learning and Applications, 2014.