You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model. You must predict the fare of a taxi trip. You need to select…

An F1 score is a classification metric, not typically used for directly evaluating continuous regression models, regardless of whether it is high or low.

An R-squared value close to 1 indicates a good regression model fit, meaning the model explains a high proportion of variance in the dependent variable.

An R-squared value close to 0 indicates a poor regression model fit, meaning the model explains very little of the variance in the dependent variable.

Root Mean Square Error (RMSE) is a standard regression metric that quantifies the average magnitude of the errors between predicted and actual values; a high value indicates a poor model fit.

A low Root Mean Square Error value indicates good regression model performance, meaning the predictions are close to the actual values.

While primarily a classification metric, an F1 score can theoretically be computed if the regression model's continuous output is subsequently binarized via a threshold, and a low score would reflect poor performance in such a derived classification task.