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.
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