Unsupervised Learning
Definition
A type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels.
Deep Dive
Unsupervised learning is a paradigm in machine learning where models are trained on datasets without any explicit labels, predefined target outputs, or human guidance. Unlike supervised learning, which relies on labeled examples to learn mapping functions, unsupervised algorithms are tasked with discovering hidden patterns, intrinsic structures, and inherent relationships within the raw, unlabeled data itself. The primary goal is to infer the underlying distribution or organization of the data, making it particularly useful for exploratory data analysis, data compression, and anomaly detection in situations where manual labeling is impractical, costly, or impossible.
Examples & Use Cases
- 1Grouping similar customer behaviors into distinct segments (e.g., "frequent buyers," "bargain hunters") based on their purchase history without pre-defined categories.
- 2Identifying fraudulent transactions by detecting data points that deviate significantly from the normal pattern of legitimate transactions.
- 3Reducing a dataset with hundreds of features describing product characteristics into a few key dimensions for easier visualization and analysis.