fetch_give_me_some_credit#

empulse.datasets.fetch_give_me_some_credit(*, backend, data_home=None, download_if_missing=True)[source]#

Fetch the “Give Me Some Credit” dataset from OpenML (binary classification).

The goal is to predict whether a customer will default on a loan in the next two years. Target variable: 1 = defaulted, 0 = no default.

Downloads data directly from OpenML without requiring scikit-learn. Downloaded data is cached locally.

Only customers with positive monthly income and a debt ratio below 1 are kept.

For a full data description and additional information about the dataset, consult the User Guide.

Classes

2

Defaulters

7616

Non-defaulters

105299

Samples

112915

Features

10
Parameters:
backendmodule

Dataframe library to use for data and target. Pass the library module directly, e.g. backend=polars or backend=pandas.

data_homestr or Path, optional

Directory used for caching downloaded data. Defaults to ~/empulse_data (or $EMPULSE_DATA_HOME).

download_if_missingbool, default=True

If False, raise an OSError when the data is not cached locally.

Returns:
datasetDataset

instance_costs contains:

  • 'cl': estimated credit line per client.

  • 'fp_cost': precomputed FP cost per client.

Notes

Cost matrix

Actual positive \(y_i = 1\)

Actual negative \(y_i = 0\)

Predicted positive \(\hat{y}_i = 1\)

tp_cost \(= 0\)

fp_cost (precomputed, depends on dataset-level statistics)

Predicted negative \(\hat{y}_i = 0\)

fn_cost \(= Cl_i \cdot L_{gd}\)

tn_cost \(= 0\)

The cost matrix uses symbolic parameters with the following defaults:

  • loss_given_default (\(L_{gd}\)) = 0.75

The following parameters are used to precompute per-instance credit lines and fp_cost values stored in instance_costs:

  • interest_rate = 0.0479 (annual)

  • fund_cost = 0.0294 (annual)

  • max_credit_line = 25000

  • term_length_months = 24

  • loan_to_income_ratio = 3

To override the symbolic default, pass the desired value when evaluating the metric:

metric(dataset.target, y_score, loss_given_default=0.6, **dataset.instance_costs)

References

[1]

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.

Examples

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))

metric = Metric(dataset.cost_matrix, Cost())
score = metric(dataset.target, y_score, **dataset.instance_costs)