A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to…

The correct answer is B. Intra-cluster. The most appropriate metric for evaluating the similarity of data points within each group in an unsupervised learning model is intra-cluster. This metric measures how similar the data points within each cluster are to one another. The goal is to have high intra-cluster…

Machine Learning (ML)

Question

A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are. Which is the MOST likely metric they should use?

Options

AROC
BIntra-cluster
CAUC
DRecall

How the community answered

(19 responses)

A
5% (1)
B
84% (16)
C
11% (2)

Explanation

The most appropriate metric for evaluating the similarity of data points within each group in an unsupervised learning model is intra-cluster. This metric measures how similar the data points within each cluster are to one another. The goal is to have high intra-cluster similarity, meaning the data points within a group should be similar to each other.

Topics

#clustering#unsupervised learning#intra-cluster distance#performance metrics

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