Learn how to use the Evaluate results section in Azure Machine Learning to visualize the average distance to cluster center and the number of points metrics for your clustering model.
Table of Contents
Question
When evaluating a clustering model, what metrics can you visualize in the Evaluate results section?
Select all that apply.
A. Maximal distance to cluster center
B. Average distance to cluster center
C. Number of points
Answer
B. Average distance to cluster center
C. Number of points
Explanation
The metrics that can be visualized in the Evaluate results section of a clustering module are: Average distance to other center, Average distance to cluster center, Number of points, Maximal distance to cluster center.
The correct answer is B and C.
When evaluating a clustering model, you can visualize the following metrics in the Evaluate results section:
- Average distance to cluster center: This metric measures the average distance of all data points in a cluster to the cluster center. The lower the average distance, the more compact the cluster is. A good clustering model should have low average distances for all clusters.
- Number of points: This metric counts the number of data points assigned to each cluster. The number of points can indicate the size and density of the clusters. A good clustering model should have balanced and reasonable numbers of points for all clusters.
You cannot visualize the maximal distance to cluster center metric in the Evaluate results section. This metric measures the farthest distance of any data point in a cluster to the cluster center. The lower the maximal distance, the more uniform the cluster is. However, this metric is not available in the Evaluate results section, and you need to use other tools or methods to calculate it.
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.
