explain

app.py

@@ -44,7 +44,7 @@ def trend(t):
   df=pd.DataFrame(result,columns=ma.columns)
   d=df.tail(1).stack(level=-1).droplevel(0, axis=0)
   '''
-  d=pd.read_pickle("data.pkl")
+  d=pd.read_pickle("./ts/data.pkl")
   '''
   https://www.gradio.app/docs/plot
         fig = px.line(df, x="day", y=countries)
@@ -230,6 +230,12 @@ This analysis is derived from an XGBoost regression model designed to predict ho
 
 Full dataset at the bottom of this tab
 
+Explain by Context
+===============
+Below are explaination in typical background E[f(x)]
+
+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
+ 
 Explain by Dataset
 ===============
 ![Summary](file=./xgb/data.png)
@@ -239,7 +245,7 @@ Explain by Dataset
 - **dist_store** demonstrates minimal impact on price.
 - Higher age correlates with lower prices while lower age raises prices.
 
-    
+
 
 Explain by Feature
 ===============
@@ -305,6 +311,7 @@ ML Observability
 
 **A/B Testing for Lift:**
 - Utilized statistical approaches in A/B testing for small business models, ensuring lift met criteria.
+- Setting up baseline model, retain evidence of input, output
 
 **File/Log Mining:**
 - Led server observability, leveraging event journey maps to understand server downtimes.
@@ -449,5 +456,5 @@ Assumptions:
 
     """)
 
-demo.launch(allowed_paths=["./xgb"])
+demo.launch(allowed_paths=["./xgb","./ts"])