update

app.py

@@ -293,7 +293,15 @@ if uploaded_file:
     # ✅ Apply Threshold to Convert Probabilities into Binary Predictions
     THRESHOLD = 0.7  # Adjust to control false positives
 
+    # ✅ Debugging: Print probability distributions before thresholding
+    print("🔍 Probability Distributions Before Thresholding:")
+    print("CatBoost:\n", pd.Series(catboost_probs).describe())
+    print("XGBoost:\n", pd.Series(xgb_probs).describe())
 
+    # ✅ Apply Threshold to Convert Probabilities into Binary Predictions
+    THRESHOLD = 0.7  # Adjust to control false positives
+    catboost_preds = (catboost_probs >= THRESHOLD).astype(int)
+    xgb_preds = (xgb_probs >= THRESHOLD).astype(int)
 
     predictions_df = pd.DataFrame({
         "CatBoost": catboost_preds,
@@ -301,8 +309,6 @@ if uploaded_file:
       #  "RandomForest": rf_preds
     })
 
-    catboost_preds = (catboost_probs >= THRESHOLD).astype(int)
-    xgb_preds = (xgb_probs >= THRESHOLD).astype(int)
     # Apply "at least one model predicts 1" rule
     predictions_df["is_click_predicted"] = predictions_df.max(axis=1)