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Answer: This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
The correct answer is B. Here's the reasoning: The overall renewal rate is 90%, meaning that if the model simply predicted that everyone would renew, it would have an accuracy of 90%. The model's accuracy for predicting renewals (82%) is lower than this baseline accuracy. The model's accuracy for predicting cancellations is high (99%), but this could be misleading because a model that predicts no cancellations at all would still have a high accuracy of 90% due to the imbalanced nature of the dataset. The high accuracy for cancellations thus may not be very informative. In summary, the model is not performing well, especially when compared to a simple baseline of always predicting renewals.
Author: LeetQuiz Editorial Team
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You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription using a machine learning model. In your exploratory data analysis, you discover an imbalanced dataset: 90% of individuals renew their subscription every year, whereas only 10% cancel. After training a Neural Network Classifier, your model achieves 99% accuracy in predicting those who cancel their subscription and 82% accuracy in predicting those who renew their subscription. Considering these results and the context of imbalanced data, how should you interpret the model's performance?
A
This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.
B
This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
C
This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
D
This is a good result because the accuracy across both groups is greater than 80%.
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