Databricks Machine Learning Associate Question 65
Select 3You are using Databricks AutoML to build a regression model to predict house prices. Which evaluation metrics can Databricks AutoML use to evaluate regression models?
- A
Mean Absolute Error (MAE)
- B
Root Mean Squared Error (RMSE)
- C
F1 Score
- D
R-squared (R²)
- E
Area Under the ROC Curve (AUC)
Show answer and explanation
Correct answers: A, B, D
Explanation
Databricks AutoML supports several evaluation metrics for regression tasks, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). These metrics are commonly used to assess the performance of regression models. However, metrics like F1 Score and Area Under the ROC Curve (AUC) are specific to classification problems and are not supported for regression tasks in Databricks AutoML.
- A. Correct.
Mean Absolute Error (MAE) is a common evaluation metric for regression tasks, used to measure the average magnitude of errors in predictions without considering their direction. Databricks AutoML supports this metric for regression problems.
- B. Correct.
Root Mean Squared Error (RMSE) is another widely used metric for regression tasks, which penalizes larger errors more than smaller ones. It is supported by Databricks AutoML for regression evaluations.
- C. Incorrect.
F1 Score is a metric used for classification tasks, not regression tasks. Therefore, it is not applicable for regression models in Databricks AutoML.
- D. Correct.
R-squared (R²) is a standard metric for regression tasks that measures the proportion of variance explained by the model. Databricks AutoML supports this metric for regression evaluations.
- E. Incorrect.
Area Under the ROC Curve (AUC) is a metric used for evaluating classification models, particularly for binary classification, and is not applicable for regression problems.