Free AWS MLA-C01 Actual Exam Questions - Question 3 Discussion
Which metric should the ML engineer use to evaluate the model's performance?
D. Since predicting prices is a regression task, classification metrics like accuracy or F1 don't apply. MAE gives a clear average error magnitude, making it the most suitable here.
D. Since this is about predicting apartment prices, which is a continuous value, you want a regression metric. Accuracy, AUC, and F1 are for classification tasks, so they’re not relevant here. MAE is straightforward—it tells you the average absolute difference between predicted and actual prices, which makes the model’s performance easier to interpret in real-world terms.
This is definitely a regression problem, so A, B, and C can be ruled out since they’re classification metrics. D makes the most sense because MAE directly measures the average prediction error in the same units as the apartment prices, which is intuitive to interpret. Even without info about outliers or error distribution, MAE is a solid general choice here. So I’m going with D too.
D, because pricing prediction is clearly a regression problem, so classification metrics don't apply.
Makes sense to go with D, MAE, since this is a regression task predicting continuous prices. Also, the other options (A, B, C) are mostly classification metrics, so they don’t apply here. Even if outliers matter, MAE gives a simple average error which works well for price predictions without over-penalizing big mistakes.
D imo, since predicting apartment prices is all about how close the predicted values are to the actual prices. MAE gives a straightforward measure of the average error without squaring it, so it won’t overly punish big mistakes like RMSE might. The other options are clearly for classification tasks, so they don’t really apply here.
Option D looks right because this is a regression problem. Accuracy, AUC, and F1 score focus on classification, so they don’t fit when predicting continuous values like prices.
Since it's predicting prices (a regression task), D (Mean absolute error) makes the most sense here. Accuracy, AUC, and F1 are for classification problems.