Accuracy metric for Clustering I have a model that outputs a latent N-dimensional embedding for all data points, trained in a way that clusters data-points from the same class together, while being separated from other clusters belonging to other different classes.
The N-dimensional embedding is projected down to 2D using UMAP. At each epoch, I wish to test the clustering capability of the model on these 2D projections for use as validation accuracy. I have the labels for each class.
How should I proceed?
You can compute &quot;Silhouette Coefficient&quot; for your aim. Its values mean:

1: Means clusters are well apart from each other and clearly distinguished.
0: Means clusters are indifferent, or we can say that the distance between clusters is not significant.
-1: Means clusters are assigned in the wrong way.

Also other measures such as purity and mutual information can be possible by ``computing an external criterion that evaluates how well the clustering matches the gold standard classes.''.