In a Spark MLlib project, you are working with a large dataset and need to perform model evaluation to assess the performance of your machine learning model. Which of the following evaluation metrics can be used in Spark MLlib for regression tasks, and how do they work?

Simulated

Mean Absolute Error (MAE), which measures the average absolute difference between the predicted and actual values.

2.3%

Mean Squared Error (MSE), which measures the average squared difference between the predicted and actual values.

5.8%

R-squared, which measures the proportion of the variance in the target variable that is explained by the model.

2.3%

All of the above, as Spark MLlib supports various evaluation metrics for regression tasks.

89.5%

Databricks Certified Machine Learning - Associate