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Chore(AI): fixed the issue

This commit is contained in:
Oumaima Fisaoui 2024-09-19 13:37:16 +01:00
parent d5c404ef04
commit e565d8b6ea
2 changed files with 2 additions and 2 deletions
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2
subjects/ai/credit-scoring/README.md
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@ -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

2
subjects/ai/credit-scoring/audit/README.md
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@ -59,7 +59,7 @@ project
```prompt
python predict.py
AUC on test set: 0.76
AUC on test set: 0.50
```