Branch-AI/one_exercise_per_file/week02/day04/ex04/audit/readme.md

1. This question is validated if the predictions on the train set and test set are:

   # 10 first values Train
   array([1, 0, 1, 1, 1, 0, 0, 1, 1, 0])
   
   # 10 first values Test
   array([1, 1, 0, 0, 0, 1, 1, 1, 0, 0])
   

2. This question is validated if the results match this output:

   F1 on the train set:  0.9911504424778761
   Accuracy on the train set:  0.989010989010989
   Recall on the train set:  0.9929078014184397
   Precision on the train set:  0.9893992932862191
   ROC_AUC on the train set:  0.9990161111794368
   
   
   F1 on the test set:  0.9801324503311258
   Accuracy on the test set:  0.9736842105263158
   Recall on the test set:  0.9866666666666667
   Precision on the test set:  0.9736842105263158
   ROC_AUC on the test set:  0.9863247863247864
   

   The confusion matrix on the test set should be:

   array([[37,  2],
          [ 1, 74]])
   

3. The ROC AUC plot should look like:

Having a 99% ROC AUC is not usual. The data set we used is easy to classify. On real data sets, always check if there's any leakage while having such a high ROC AUC score.