Databricks Machine Learning Associate Question 62
Select 4You are using Databricks AutoML to train a regression model to predict house prices based on various features. Which evaluation metrics can Databricks AutoML use to assess the performance of your regression model?
- A
Mean Absolute Error (MAE)
- B
Area Under the ROC Curve (AUC-ROC)
- C
R-squared (R²)
- D
Root Mean Squared Error (RMSE)
- E
Precision
- F
Mean Squared Error (MSE)
Show answer and explanation
Correct answers: A, C, D, F
Explanation
Databricks AutoML supports multiple evaluation metrics for regression, including MAE, R², RMSE, and MSE. These metrics help evaluate the accuracy and variance of predictions in regression problems. Metrics like AUC-ROC and Precision are specific to classification tasks and are not applicable for regression models.
- A. Correct.
Mean Absolute Error (MAE) is a commonly used evaluation metric for regression problems, and Databricks AutoML supports it for assessing regression models.
- B. Incorrect.
Area Under the ROC Curve (AUC-ROC) is a classification metric and is not applicable to regression problems, so it is not used by Databricks AutoML for regression.
- C. Correct.
R-squared (R²) is a standard metric for regression problems to evaluate the proportion of variance explained by the model, and it is supported by Databricks AutoML.
- D. Correct.
Root Mean Squared Error (RMSE) is another widely used regression metric that evaluates the model's prediction error, and Databricks AutoML supports it.
- E. Incorrect.
Precision is a classification metric that measures the proportion of true positives among predicted positives, so it is not applicable to regression problems.
- F. Correct.
Mean Squared Error (MSE) is a common metric for quantifying prediction error in regression models, and Databricks AutoML supports it.