In what scenario would you choose unsupervised learning?

Unsupervised learning is specifically designed for situations where you do not have labeled outputs in your dataset. This approach is ideal for discovering hidden patterns or intrinsic structures within the data. In scenarios like customer segmentation, anomaly detection, or clustering, the goal is to analyze the input data and identify patterns or groupings without any predefined classifications.

This technique enables you to explore the data and learn from it in a way that is not reliant on prior labels or outcomes. As a result, unsupervised learning can uncover insights that may not be immediately apparent, facilitating tasks such as association rule learning or dimensionality reduction.

Choosing unsupervised learning is appropriate when you are looking to extract meaningful information or structure from unlabeled data, making it a powerful tool for exploratory data analysis and pattern recognition.