The correct options are An R squared value near one and A low Root Mean Squared Error value.

An R squared value near one indicates that the regression model explains a large proportion of the variance in trip fares which means the model captures the relationships in the data well. R squared is a relative measure of explained variance and values closer to one reflect a better fit for continuous targets like fare amounts.

A low Root Mean Squared Error value means the average magnitude of the prediction errors is small and the errors are measured in the same units as the target so a lower RMSE directly corresponds to predictions that are closer to actual fare values. RMSE also penalizes larger errors more strongly which is useful when big prediction mistakes matter.

A large Root Mean Squared Error value is incorrect because a large RMSE signals large prediction errors and therefore poor regression performance which is the opposite of what you want when evaluating a fare prediction model.

A high F1 score is incorrect because the F1 score measures precision and recall for classification tasks and it does not apply to regression problems with continuous targets like trip fares.

An R squared value close to zero is incorrect because a value near zero means the model explains almost none of the variance in the target and therefore performs poorly at predicting fares.

A low F1 score is incorrect because a low F1 indicates poor classification performance and it is not relevant for evaluating a regression model.

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