Answer-first summary for fast verification
Answer: a Root Mean Square Error value that is low, an R-Squared value close to 1
For a regression model predicting taxi fares, the appropriate metrics are those that measure prediction accuracy for continuous numerical values. Root Mean Square Error (RMSE) measures the average magnitude of prediction errors, with lower values indicating better model performance. R-Squared (R²) measures the proportion of variance in the dependent variable that is predictable from the independent variables, with values close to 1 indicating better model fit. F1 score is inappropriate here as it's a classification metric that combines precision and recall, which don't apply to regression problems. The community discussion shows 100% consensus on AD, with explanations confirming RMSE should be low and R-Squared should be close to 1 for good regression performance.
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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