What is Unsupervised Learning?
Is a category of machine learning algorithms that discovers patterns, structure, and relationships in unlabeled data, and without predefined target outputs. The goal is to discover knowledge from the data itself, usually by grouping similar data points together.
Algorithms
These are some examples of unsupervised learning algorithms:
Metrics
Because of it’s unsupervised nature, there are no standard metrics to evaluate the performance of unsupervised learning algorithms such as accuracy or precision. There is, however, a set of metrics that can be used to evaluate the quality of the results.
Applications
Unsupervised learning is used in a variety of applications, including:
- Customer segmentation: Grouping customers based on purchasing behavior or demographics.
- Anomaly detection: Identifying unusual patterns or outliers in data, such as fraud detection.
- Recommendation systems: Suggesting products or content to users based on their preferences and behavior.
- Market basket analysis: Discovering associations between products that are frequently purchased together.