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AI-900: Azure ML Regression Model Evaluation Metrics

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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Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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