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Answer: Conduct a statistical hypothesis test, such as a paired t-test or McNemar's test, to evaluate the significance of the performance difference between the two models, considering the risk of errors., Use a t-test to compare the means of the model's performance metrics with and without the new feature, as it directly tests for differences in means.
Correct Option: C. Conduct a statistical hypothesis test, such as a paired t-test or McNemar's test, to evaluate the significance of the performance difference between the two models, considering the risk of errors. Explanation: In this scenario, the primary concern is to statistically validate whether the addition of a new feature leads to a significant improvement in the model's performance, while also being mindful of Type I and Type II errors. A statistical hypothesis test is the most appropriate method for this purpose because it provides a framework to assess the significance of the observed differences under uncertainty. Specifically, a paired t-test or McNemar's test can be used depending on the nature of the performance metrics (continuous or binary, respectively). Why other options are not correct: - **A. Use a t-test to compare the means of the model's performance metrics with and without the new feature, as it directly tests for differences in means.**: While a t-test can compare means, it does not account for the paired nature of the data (same dataset used for both models) or the specific concerns about Type I and II errors without additional considerations. - **B. Apply cross-validation to ensure the model's performance is consistent across different subsets of the data, but do not perform any statistical tests.**: Cross-validation is important for assessing model consistency but does not provide a statistical test for significance of performance differences. - **D. Optimize the model's hyperparameters to maximize performance, assuming that any improvement is significant without statistical validation.**: Hyperparameter optimization is crucial for model performance but does not replace the need for statistical validation of performance improvements. - **E. Both A and C are correct because they each address different aspects of the problem: A tests for mean differences, while C evaluates significance considering error risks.**: While both A and C involve statistical tests, C is more comprehensive as it specifically addresses the scenario's requirements, including the consideration of error risks.
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In the development of a machine learning model for predicting customer churn, you are tasked with evaluating whether a new feature significantly improves the model's performance compared to the baseline model without the feature. The dataset is large, and the team is concerned about both Type I and Type II errors. Which of the following approaches is the MOST appropriate for this scenario, and why? Choose the best option.
A
Use a t-test to compare the means of the model's performance metrics with and without the new feature, as it directly tests for differences in means.
B
Apply cross-validation to ensure the model's performance is consistent across different subsets of the data, but do not perform any statistical tests.
C
Conduct a statistical hypothesis test, such as a paired t-test or McNemar's test, to evaluate the significance of the performance difference between the two models, considering the risk of errors.
D
Optimize the model's hyperparameters to maximize performance, assuming that any improvement is significant without statistical validation.
E
Both A and C are correct because they each address different aspects of the problem: A tests for mean differences, while C evaluates significance considering error risks.