5.4. 2011 Kaggle competition Give Me Some Credit#

5.4.1. 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.

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

5.4.2. Using the Dataset#

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

data: the feature matrix
target: the target vector
tp_cost: the cost of a true positive
fp_cost: the cost of a false positive
fn_cost: the cost of a false negative
tn_cost: the cost of a true negative
feature_names: the feature names
target_names: the target names
DESCR: the full description of the dataset

from empulse.datasets import load_give_me_some_credit

dataset = load_give_me_some_credit()

Alternatively, the load function can also return the features, target, and costs separately, by setting return_X_y_costs=True. Additionally, you can specify that you want the output in a pandas.DataFrame format, by setting as_frame=True.

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

from empulse.datasets import load_give_me_some_credit
from empulse.models import CSLogitClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

X, y, tp_cost, fp_cost, fn_cost, tn_cost = load_give_me_some_credit(
    return_X_y_costs=True,
    as_frame=True
)
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', CSLogitClassifier())
])
pipeline.fit(
    X,
    y,
    model__tp_cost=tp_cost,
    model__fp_cost=fp_cost,
    model__fn_cost=fn_cost,
    model__tn_cost=tn_cost
)

5.4.3. 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].

These assumptions can be changed by passing your own values to the load_give_me_some_credit function:

from empulse.datasets import load_give_me_some_credit

X, y, tp_cost, fp_cost, fn_cost, tn_cost = load_give_me_some_credit(
    return_X_y_costs=True,
    interest_rate=0.0479,
    fund_cost=0.0294,
    max_credit_line=25000,
    loss_given_default=0.75,
    term_length_months=24,
    loan_to_income_ratio=3,
)

5.4.4. 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