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AI-900: Evaluating Clustering Models in Azure ML Essential Metrics Explained

Learn about the key evaluation metrics Azure ML employs for Clustering models, including insights into point count, cluster center distance, and comprehensive evaluation techniques for accurate model assessment.

Table of Contents

Question

What metrics does Azure ML use for the evaluation of the Clustering models?

Select all that apply.

A. Root Mean Squared Error (RMSE)
B. Number of Points
C. Accuracy
D. Combined Evaluation
E. Precision
F. Coefficient of Determination
G. Average Distance to Cluster Center

Answer

B. Number of Points
D. Combined Evaluation
G. Average Distance to Cluster Center

Explanation

Azure ML uses model evaluation for the measurement of the trained model accuracy.

For Clustering models, the Evaluate Model module provides the following five metrics: Average Distance to Other Center, Average Distance to Cluster Center, Number of Points, Maximal Distance to Cluster Center, Combined Evaluation.

Option B is correct. Number of Points is the clustering model evaluation metrics. It represents how many data points are assigned to the cluster.
Option D is correct. Combined Evaluation is the clustering model evaluation metrics. It represents an average score for the model clusters.
Option G is correct. Average Distance to Cluster Center is the clustering model evaluation metrics. It represents the average distance from the center of the cluster to each data point.
Option A is incorrect. 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. It is not the clustering model evaluation metrics.
Option C is incorrect. Accuracy is the classification model evaluation metrics. It represents how many cases the model predicted right proportionally to the total number of cases. It is not the clustering model evaluation metrics.
Option E is incorrect. Precision is the classification model evaluation metrics. It represents how many positive cases are predicted right. It is not the clustering model evaluation metrics.
Option F is incorrect. Coefficient of determination or R2 is the regression model evaluation metrics. It is not the clustering model evaluation metrics.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.

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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