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Author | SHA1 | Date |
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Oumaima Fisaoui | ed6cfdb2a1 | |
Oumaima Fisaoui | e565d8b6ea |
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@ -22,7 +22,7 @@ There are 3 expected deliverables associated with the scoring model:
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- The trained machine learning model with the features engineering pipeline:
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- Do not forget: **Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering.**
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- The model is validated if the **AUC on the test set is higher than 75%**.
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- The model is validated if the **AUC on the test set is higher than 50%**.
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- 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.
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#### b - Kaggle submission
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@ -59,7 +59,7 @@ project
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```prompt
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python predict.py
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AUC on test set: 0.76
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AUC on test set: 0.50
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```
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