Databricks Machine Learning Associate Question 61
Select 3You are using Databricks AutoML to train a regression model on a dataset predicting house prices. After the training process, AutoML provides evaluation metrics to assess model performance. Which of the following metrics can AutoML use for regression problems?
- A
Mean Absolute Error (MAE)
- B
Root Mean Squared Error (RMSE)
- C
Accuracy
- D
R-squared (R²)
- E
Confusion Matrix
Show answer and explanation
Correct answers: A, B, D
Explanation
Databricks AutoML uses regression-specific metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) to evaluate the performance of regression models. Metrics like Accuracy and Confusion Matrix are designed for classification problems and are not applicable to regression tasks.
- A. Correct.
Mean Absolute Error (MAE) is a common regression metric that evaluates the average magnitude of errors in predictions without considering their direction. AutoML can use this metric to assess regression models.
- B. Correct.
Root Mean Squared Error (RMSE) is another standard regression metric that measures the square root of the average squared differences between predictions and actual values. AutoML includes this as a performance evaluation metric for regression problems.
- C. Incorrect.
Accuracy is primarily used for classification tasks, not regression. It measures the percentage of correctly classified instances, which is not applicable to regression problems.
- D. Correct.
R-squared (R²) is a widely used metric for regression that indicates the proportion of variance in the dependent variable explained by the independent variables. AutoML can use this to evaluate model performance for regression.
- E. Incorrect.
Confusion Matrix is a tool for evaluating classification models and provides insights into true positives, false positives, true negatives, and false negatives. It is irrelevant for regression tasks.