How do you all approach measuring performance with an unsupervised approach?

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Compare it with a baseline.

Baseline can be: randomly chosen or a rule-based approach or a supervised approach.

Underpromise, overdeliver

Lol it took me a while to get what C1 was saying…

But OP, did you mean: no supervisor OR unsupervised learning?

Apologies for the lack of clarity. I meant unsupervised learning!

Classification metrics if you're trying these algos as just something else to try on a classification task. But that's very very unreliable, so... Don't.

Otherwise, look at any number of algo appropriate metrics that are basically what makes the algo work in the first place. Apriori has support and confidence, clustering has a huge number of cluster distance and similarity metrics, PCA and friends have their own stuff with variance in the eigenvectors, information theoretic methods have their own similarity metrics they use, autoencoders use whatever similarity metrics you opt for, etc etc....

Thank you for the detailed reply, I’ll use these as a starting point!

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