A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluat

InferenceLatency measures the time taken for a model to make a prediction, which is an operational metric, not a measure of the model's predictive performance or accuracy.

Mean Squared Error (MSE) is a standard regression metric that measures the average of the squared differences between predicted and actual values. It quantifies the average magnitude of the errors and is commonly used to evaluate the accuracy of regression models.

Root Mean Squared Error (RMSE) is the square root of MSE, providing a metric in the same units as the target variable (minutes, in this case). RMSE is widely used in regression tasks for its interpretability and represents the typical magnitude of prediction errors.

Precision is a classification metric that measures the proportion of true positive predictions among all positive predictions, making it unsuitable for regression problems where continuous values are predicted.

Accuracy is a classification metric that measures the overall proportion of correct predictions, which is not applicable to regression models that predict continuous numerical outcomes.