Databricks Machine Learning Associate Question 605
Select 3You are training a regression model in Databricks to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you evaluate it using the following metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Which of the following statements correctly interprets these metrics?
- A
A lower RMSE indicates that the model's predictions are closer to the actual values on average.
- B
MAE measures the average of the squared differences between predicted and actual values.
- C
R-squared represents the proportion of variance in the target variable that is explained by the model.
- D
A higher R-squared value indicates that the model has better predictive performance.
- E
MAE is more sensitive to outliers compared to RMSE.
Show answer and explanation
Correct answers: A, C, D
Explanation
Regression metrics like RMSE, MAE, and R-squared provide key insights into the performance of a regression model. RMSE quantifies the spread of prediction errors, MAE measures average prediction error without squaring, and R-squared explains how well the model captures the variance in the target variable. A good understanding of these metrics helps assess model quality and identify areas for improvement.
- A. Correct.
Correct: RMSE measures the standard deviation of the residuals (prediction errors). A lower RMSE indicates better model performance as the predictions are closer to the actual values.
- B. Incorrect.
Incorrect: MAE measures the average of the absolute differences between predicted and actual values, not the squared differences.
- C. Correct.
Correct: R-squared quantifies the proportion of the variance in the dependent variable that is explained by the independent variables in the model.
- D. Correct.
Correct: A higher R-squared value generally indicates that the model explains more variability in the target variable, suggesting better performance.
- E. Incorrect.
Incorrect: RMSE is more sensitive to outliers than MAE because it squares the errors, amplifying larger deviations.