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1.3 KiB
The exercice is validated is all questions of the exercice are validated
The question 1 is validated if the predictions on the train set and test set are:
```console
# 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])
```
The question 2 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 question 2 is validated if the results match the confusion matrix on the test set should be:
array([[37, 2],
[ 1, 74]])
The question 3 is validated if the ROC AUC plot looks like the plot below:
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.