---
tags:
- tabular-classification
- credit-scoring
- fairness
task:
  type: tabular-classification
  name: Creditworthiness Prediction
metrics:
- name: Accuracy
  type: accuracy
  value: "<value from evaluate_model>"
  note: The proportion of correctly classified instances.
- name: Precision
  type: precision
  value: "<value from evaluate_model>"
  note: The proportion of positive identifications that were actually correct.
- name: Recall
  type: recall
  value: "<value from evaluate_model>"
  note: The proportion of actual positive cases that were identified correctly.
- name: F1 Score
  type: f1
  value: "<value from evaluate_model>"
  note: The harmonic mean of Precision and Recall.
- name: Selection Rate
  type: selection_rate
  value: "<fairlearn value>"
  note: The proportion of predictions that are positive, for each group.
- name: Equal Opportunity
  type: true_positive_rate
  value: "<fairlearn value>"
  note: The proportion of actual positive outcomes that are correctly identified for each group.
---

# Creditworthiness Prediction

This model predicts whether an applicant is creditworthy based on tabular financial and demographic features.