Azure Data Scientists Associate Practice Exam

What method should be used to log the Root Mean Squared Error (RMSE) in MLflow?

1. mlflow.log_param()

2. mlflow.log_artifact()

3. mlflow.log_metric()

4. mlflow.log_run()

The correct answer is: mlflow.log_metric()

To log the Root Mean Squared Error (RMSE) in MLflow, the most appropriate method is mlflow.log_metric(). RMSE is a quantitative measure used to assess the performance of a regression model by providing a measure of how well the model predicts the target variable. As a metric, it represents a numerical value that indicates the average deviation of the predicted values from the actual values. The method mlflow.log_metric() is specifically designed to log metrics like RMSE, accuracy, loss values, or any other numerical performance indicators during the training or evaluation phase of a machine learning model. This function allows users to record the values of such metrics alongside other experiment data, which can then be accessed for analysis or visualization later. Other methods, such as logging parameters (mlflow.log_param()), are used to record hyperparameters or configuration settings, while mlflow.log_artifact() is utilized for saving files like models or datasets. The mlflow.log_run() is focused on tracking a single run's context but does not log metrics directly. Therefore, for capturing performance metrics like RMSE, mlflow.log_metric() is the correct and most relevant choice.