fetch_home_equity#

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

Fetch the Home Equity (HMEQ) dataset from OpenML (binary classification).

The goal is to predict whether a home equity loan applicant will default or be seriously delinquent. Target variable: 1 = default, 0 = no default.

For additional information about the dataset, consult the User Guide.

Classes

2

Defaults

1189

Non-defaults

4771

Samples

5960

Features

12
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': loan amount per borrower (LOAN).

  • 'fp_cost': precomputed false positive cost per borrower.

Notes

Cost matrix (Bahnsen et al. 2014, Ballegeer et al. 2025):

Actual positive \(y_i = 1\)

Actual negative \(y_i = 0\)

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

tp_cost \(= 0\)

fp_cost (precomputed per borrower)

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

References

[1]

Baesens, B., Roesch, D., & Scheule, H. (2016). Credit risk analytics: Measurement techniques, applications, and examples in SAS. John Wiley & Sons.

[2]

Ballegeer, M., Bogaert, M., & Benoit, D. F. (2025). Evaluating the stability of model explanations in instance-dependent cost-sensitive credit scoring. European Journal of Operational Research, 326(2), 630–640.

Examples

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

dataset = fetch_home_equity(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)
On this page