What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k- means clustering, given metrics for the cost of the clustering (how well it fits the data) and…

The correct answer is A. The lowest cost clustering subject to a stability constraint. There is a tradeoff between cost and stability in unsupervised learning. The more tightly you fit the data, the less stable the model will be, and vice versa. The idea is to find a good balance with more weight given to the cost. Typically a good approach is to set a stability…

Model Evaluation and Selection

Question

What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k- means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?

Options

AThe lowest cost clustering subject to a stability constraint
BThe lowest cost clustering
CThe most stable clustering subject to a minimal cost constraint
DThe most stable clustering

How the community answered

(61 responses)

A
79% (48)
B
13% (8)
C
5% (3)
D
3% (2)

Explanation

There is a tradeoff between cost and stability in unsupervised learning. The more tightly you fit the data, the less stable the model will be, and vice versa. The idea is to find a good balance with more weight given to the cost. Typically a good approach is to set a stability threshold and select the model that achieves the lowest cost above the stability threshold.

Topics

#K-means evaluation#clustering stability#cost optimization#unsupervised model quality

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