Databricks Machine Learning Associate Question 602
Select 3You are training a regression model to predict house prices based on features such as square footage, number of bedrooms, and location. After training the model, you evaluate its performance using multiple metrics. Which of the following metrics indicate how well the model explains the variance in the target variable and how far the predictions are from the actual values on average?
- A
Root Mean Squared Error (RMSE)
- B
Mean Absolute Error (MAE)
- C
R-squared
- D
Precision
- E
Mean Squared Error (MSE)
Show answer and explanation
Correct answers: A, B, C
Explanation
In regression problems, metrics like RMSE and MAE are used to evaluate the average magnitude of prediction errors, while R-squared is used to understand how well the model explains the variance in the target variable. Precision is irrelevant for regression tasks as it is a classification metric, and while MSE is related to RMSE, it is less interpretable because it is not in the same unit as the target variable.
- A. Correct.
Root Mean Squared Error (RMSE) measures the average magnitude of the prediction errors, with larger errors being penalized more heavily due to squaring.
- B. Correct.
Mean Absolute Error (MAE) calculates the average absolute difference between predicted and actual values, making it a useful metric for understanding average prediction error.
- C. Correct.
R-squared explains the proportion of the variance in the target variable that is captured by the model, providing a measure of the model’s explanatory power.
- D. Incorrect.
Precision is a classification metric that measures the proportion of true positive predictions out of all positive predictions, and it is not applicable to regression problems.
- E. Incorrect.
Mean Squared Error (MSE) measures the average squared difference between predictions and actual values, but it is not as interpretable as RMSE or MAE in terms of the unit of the target variable.