Update README.md

README.md

@@ -1,44 +1,38 @@
-{
-  "task": {
-    "type": "tabular-classification",
-    "name": "Creditworthiness Prediction"
-  },
-  "metrics": [
-    {
-      "name": "Accuracy",
-      "type": "accuracy_score",
-      "value": "<value from evaluate_model>",
-      "note": "The proportion of correctly classified instances."
-    },
-    {
-      "name": "Precision",
-      "type": "precision_score",
-      "value": "<value from evaluate_model>",
-      "note": "The proportion of positive identifications that were actually correct."
-    },
-    {
-      "name": "Recall",
-      "type": "recall_score",
-      "value": "<value from evaluate_model>",
-      "note": "The proportion of actual positive cases that were identified correctly."
-    },
-    {
-      "name": "F1 Score",
-      "type": "f1_score",
-      "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."
-    }
-  ]
-}
+---
+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.