Davies-Bouldin Index higher or lower score better [closed]

Question

I trained a KMEANS clustering model using Google Bigquery, and it gives me these metrics in the evaluation tab of my model. My question is are we trying to maximize or minimize Davies-Bouldin index and mean-squared distance?

Overcompensation

Read up about this metrics, and choose some simpler metrics like co variance instead. – Tauten

Answer 1

Davies-Bouldin index is a validation metric that is often used in order to evaluate the optimal number of clusters to use. It is defined as a ratio between the cluster scatter and the cluster’s separation and a lower value will mean that the clustering is better.

Regarding the second metric, the mean squared distance makes reference to the intra cluster variance, which we want to minimize as a lower WCSS (within-cluster sums of squares) will maximize the distance between clusters.

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Crowl