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Answer: All of the above
In a regression problem, all the evaluation metrics A, B, and C can be used to assess the performance of the ensemble model. MAE measures the average absolute difference between the predicted and actual values. RMSE measures the square root of the average squared difference between the predicted and actual values. R2 measures the proportion of the variance in the dependent variable that is predictable from the independent variables. All these metrics provide different perspectives on the model's performance, making option D the correct choice.
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In a regression problem, you have trained an ensemble of models using the boosting technique. You want to evaluate the performance of the ensemble model. Which of the following evaluation metrics would be most appropriate, and explain why?
A
Mean Absolute Error (MAE)
B
Root Mean Squared Error (RMSE)
C
R-squared (R2)
D
All of the above
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