What are the best ways to ensure model interpretability in unsupervised learning?

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Unsupervised learning is a branch of machine learning that deals with finding patterns and structures in data without using labels or predefined rules. It can be useful for tasks such as clustering, dimensionality reduction, anomaly detection, and feature extraction. However, unlike supervised learning, where the model's performance can be measured by comparing its predictions with the true outcomes, unsupervised learning models are often hard to interpret and explain. How can you ensure that your unsupervised learning model is not only accurate, but also understandable and trustworthy? Here are some best practices and tools that can help you achieve model interpretability in unsupervised learning.

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