Exam Professional Machine Learning Engineer topic 1 question 141 discussion

if we suppose the case where the model simply predicted that everyone would renew, the renewals rate should be always higher than the cancellations. Therefore, This case means that the model made some asumptions about how a cancellation looks like and misled some of the renewals cases (it could make some wrong asumptions because there are few data)

C suggests that predicting cancellations is more difficult due to less data for this group. While it's true that imbalanced datasets, where one class is underrepresented, can pose challenges for machine learning models, the key issue here is that the model's accuracy for predicting renewals is lower than the accuracy for predicting cancellations. In this scenario, the imbalance alone does not explain the lower accuracy for renewals. The model should ideally perform well on both classes, and the fact that it doesn't, especially when compared to a simple baseline of always predicting renewals (which would have an accuracy of 90%), suggests that there's a problem with the model's performance. Therefore, option B is a more appropriate interpretation, highlighting that the model is performing worse than a basic strategy of always predicting renewals.

The goal of the model is to predict cancellations, not renewals...

I think C is the most likely. we are experiencing an imbalanced dataset and the target class is canceled. Actually this case we have to use other metrics like F1 and precision/recall If you want to get ReNew accuracy higher than CAncel, you have to make it greater than 99% as compared to another. it is hard to archive.