While training a regression model that predicts delivery time in minutes, which evaluation metrics should the data scientist use? (Choose two.)

For a regression model predicting a continuous value like delivery time, Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are appropriate evaluation metrics. MSE calculates the average of the squared differences between predicted and actual values, penalizing larger errors more heavily. RMSE is the square root of MSE, providing an error metric in the same units as the target variable, making it more interpretable. InferenceLatency measures the time taken for a model to make a prediction, which is a performance metric, not an evaluation of model accuracy. Precision and Accuracy are metrics used for classification models, where the goal is to predict discrete classes, not continuous values.