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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])
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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]])
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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.