You manage an Azure Machine Learning workspace. You develop a regression model training pipeline by using Notebooks. You need to determine the appropriate evaluation metric for the experiment. Which…

The correct answer is B. correlation C. residuals. Regression models predict continuous numeric values, so their evaluation metrics differ from classification metrics. Correlation (R or R²) measures how well the predicted values correlate with actual values - a standard regression metric. Residuals (the differences between…

Explore data and run experiments

Question

You manage an Azure Machine Learning workspace. You develop a regression model training pipeline by using Notebooks. You need to determine the appropriate evaluation metric for the experiment. Which two metrics should you choose? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

Aprecision
Bcorrelation
Cresiduals
Drecall

How the community answered

(26 responses)

A
12% (3)
B
81% (21)
D
8% (2)

Explanation

Regression models predict continuous numeric values, so their evaluation metrics differ from classification metrics. Correlation (R or R²) measures how well the predicted values correlate with actual values - a standard regression metric. Residuals (the differences between predicted and actual values) are fundamental to diagnosing regression model performance and detecting bias or variance issues. Precision (A) and Recall (D) are classification metrics used with discrete class labels, and are not appropriate for evaluating regression models.

Topics

#Regression models#Evaluation metrics#Azure Machine Learning#Experimentation

Community Discussion

No community discussion yet for this question.

Full DP-100 Practice