explain

app.py

@@ -232,13 +232,27 @@ Full dataset at the bottom of this tab
 
 Explain by Context
 ===============
-- Below are explanation in typical background E[f(x)]
+- Sometimes, understanding why an individual defaults requires shifting to a credit-healthy background, altering the baseline E[f(x) | credit healthy] using interventional feature perturbation ([source](https://arxiv.org/pdf/2006.16234.pdf)).
+
+[UCI Machine Learning Repository - Credit Default Dataset](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset)
+
+![Credit Record Summary](file=./xgb/credit_record.png)
+**Observations from a healthy credit background:**
+- This individual defaults due to **PAY_0=2** and **PAY_6=2**.
+- PAY_0 represents repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above).
 
-- Sometime it is useful to switch to credit healthy background, to explain why a certain person default by changing the baseline E[f(x) | credit healthy] with interventional feature perturbation
-https://arxiv.org/pdf/2006.16234.pdf
+![Credit Data Summary](file=./xgb/credit_data.png)
+**Insights from a healthy credit background:**
+- Default patterns relate to high **PAY_0/PAY_2** (payment delay) and low **LIMIT_BAL** (lack of liquidity).
+- LIMIT_BAL signifies the amount of given credit in NT dollars (includes individual and family/supplementary credit).
+- BILL_AMT1 indicates the bill statement amount in September, 2005 (NT dollar).
+
+This approach leverages context-specific explanations within credit health parameters to reveal why individuals default, providing valuable insights into repayment behavior and financial health.
  
 Explain by Dataset
 ===============
+- Below are explanation in typical background E[f(x)]
+
 ![Summary](file=./xgb/data.png)
 
 **Key insights:**