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Answer: a Root Mean Square Error value that is low, an R-Squared value close to 1
The question asks for two performance metrics to evaluate a regression model predicting taxi fares. For regression problems, appropriate metrics include Root Mean Square Error (RMSE) and R-Squared. A low RMSE indicates small prediction errors, which is desirable. An R-Squared value close to 1 indicates that the model explains a high proportion of the variance in the target variable, which is optimal. Options B and F are incorrect because a high RMSE or R-Squared close to 0 indicate poor model performance. Options C and E (F1 score) are unsuitable as F1 score is for classification models, not regression, as confirmed by community comments highlighting this distinction.
Author: LeetQuiz Editorial Team
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You are developing a regression model to predict taxi fares from a historical dataset. You need to select two performance metrics to evaluate the regression model.
Which two metrics should you use? Each correct answer presents a complete solution.
A
a Root Mean Square Error value that is low
B
an R-Squared value close to 0
C
an F1 score that is low
D
an R-Squared value close to 1
E
an F1 score that is high
F
a Root Mean Square Error value that is high
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