Discover the essential metrics Azure ML employs for evaluating regression models. Understand how RMSE, MAE, and Coefficient of Determination play vital roles in assessing model performance.
Table of Contents
Question
What metrics does Azure ML use for the Evaluation of the regression models?
Select all that apply.
A. Root Mean Squared Error (RMSE)
B. Accuracy
C. Number of Points
D. Mean Absolute Error (MAE)
E. Combined Evaluation
F. Coefficient of Determination
G. Recall
Answer
Coefficient of Determination, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
Explanation
Azure ML uses model evaluation for the measurement of the trained model accuracy. For regression models Evaluate Model module provides the following five metrics: Mean absolute error (MAE), Root mean squared error (RMSE), Relative absolute error (RAE), Relative squared error (RSE), and Coefficient of determination (R2).
- Root Mean Squared Error (RMSE) is the regression model evaluation metrics. It represents the square Root from the squared mean of the errors between predicted and actual values.
- Mean absolute error (MAE) is the regression model evaluation metrics. It produces the score that measures how close the model is to the actual values — the lower score, the better the model performance.
- Coefficient of determination or R2 is the regression model evaluation metrics. It reflects the model performance: the closer R2 to 1 – the better the model fits the data.
- Accuracy is the classification model evaluation metrics and is not the regression model evaluation metrics.
- Number of Points is the clustering model evaluation metrics and is not the regression model evaluation metrics.
- Combined Evaluation is the clustering model evaluation metrics and is not the regression model evaluation metrics.
- Recall is the classification model evaluation metrics and is not the regression model evaluation metrics.
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