Merge e565d8b6ea into 2f7977a95a

subjects/ai/credit-scoring/README.md

@@ -22,7 +22,7 @@ There are 3 expected deliverables associated with the scoring model:
 - The trained machine learning model with the features engineering pipeline:
 
   - Do not forget: **Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering.**
-  - The model is validated if the **AUC on the test set is higher than 75%**.
+  - The model is validated if the **AUC on the test set is higher than 50%**.
   - The labelled test data is not publicly available. However, a Kaggle competition uses the same data. The procedure to evaluate test set submission is the same as the one used for the project 1.
 
 #### b - Kaggle submission

subjects/ai/credit-scoring/audit/README.md

@@ -59,7 +59,7 @@ project
 ```prompt
     python predict.py
 
-    AUC on test set: 0.76
+    AUC on test set: 0.50
 
 ```