Databricks Machine Learning Associate Question 64
Select 4A data scientist is using Databricks AutoML to build a regression model. Which of the following evaluation metrics can AutoML use to evaluate regression problems?
- A
Mean Absolute Error (MAE)
- B
Root Mean Squared Error (RMSE)
- C
F1 Score
- D
R-squared (R²)
- E
Accuracy
- F
Mean Squared Error (MSE)
Show answer and explanation
Correct answers: A, B, D, F
Explanation
Databricks AutoML supports a variety of regression evaluation metrics, including MAE, RMSE, R², and MSE. These metrics are designed specifically for regression problems to measure prediction error or model fit. Metrics such as F1 Score and Accuracy are designed for classification tasks and are not applicable to regression models.
- A. Correct.
Mean Absolute Error (MAE) is a common metric for regression models and is supported by Databricks AutoML for evaluating regression problems.
- B. Correct.
Root Mean Squared Error (RMSE) is another widely used metric for regression models and is supported by Databricks AutoML.
- C. Incorrect.
F1 Score is a classification metric, not a regression metric, so it is not applicable for regression problems.
- D. Correct.
R-squared (R²) is a standard metric for evaluating regression models and is supported by Databricks AutoML.
- E. Incorrect.
Accuracy is a metric used for classification problems, not regression problems, so it is not applicable here.
- F. Correct.
Mean Squared Error (MSE) is a common regression evaluation metric and is supported by Databricks AutoML.