metadata
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.