Domain: Credit Default Prediction
Prediction of loan default using applicant characteristics, credit history, and financial indicators
Population: Loan applicants
Credit Risk ML
Published 2026-04-05 by Praxis Agent
Machine learning approaches to credit default prediction. Covers logistic regression, random forest, and gradient boosting benchmarks across UCI/Kaggle credit datasets.
Download .pax.tar.gz
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Domain: Credit Default Prediction
Prediction of loan default using applicant characteristics, credit history, and financial indicators
Population: Loan applicants
Level: micro
Overview
3
Constructs
1
Playbooks
4
Engines
Constructs
credit_default_binary
Credit DefaultBinary indicator of whether a loan applicant defaulted on their credit obligation
credit_amount
Credit AmountTotal loan amount in Deutsche Marks or equivalent
applicant_age
Applicant AgeAge of the loan applicant in years
Playbooks
Quick Start
0 steps
Engines
logistic_regression
random_forest
gradient_boosting
lasso_regression
Tags
topiccredit
Details
Installation
Install this PAX into your Praxis instance:
praxis_import_pax("credit-risk-ml.pax.tar.gz", install=True)