Databricks Machine Learning Associate Question 604
Select 3A data scientist is evaluating the performance of a regression model predicting house prices based on historical data. They calculate the following metrics for the model: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. Which of the following statements correctly interprets these metrics?
- A
A lower RMSE indicates better predictive performance.
- B
MAE penalizes larger errors more heavily than RMSE.
- C
R-squared represents the proportion of variance in the target variable explained by the model.
- D
An R-squared value of 0.95 indicates the model explains almost all variability in the target variable.
Show answer and explanation
Correct answers: A, C, D
Explanation
Understanding regression metrics is crucial for evaluating model performance. RMSE measures the average magnitude of prediction errors, penalizing large errors more heavily than MAE. R-squared provides a measure of how well the model explains the variability in the target variable. Correctly interpreting these metrics ensures the data scientist can assess model effectiveness appropriately.
- A. Correct.
Correct. Lower RMSE values indicate the model's predictions are closer to the actual values, reflecting better performance.
- B. Incorrect.
Incorrect. RMSE penalizes larger errors more heavily due to the squaring of errors, whereas MAE provides a linear penalty.
- C. Correct.
Correct. R-squared quantifies the proportion of variance in the dependent variable that can be explained by the independent variables in the model.
- D. Correct.
Correct. An R-squared value of 0.95 suggests that 95% of the variability in the target variable is explained by the model, indicating strong predictive performance.