nonsupervising
Nonsupervising refers to a learning paradigm in artificial intelligence and machine learning where an algorithm learns patterns and structures from data without explicit labels or guidance from a human supervisor. Unlike supervised learning, where data is paired with correct outputs, nonsupervised methods are presented with raw data and are tasked with discovering inherent relationships, groupings, or anomalies within it.
The primary goal of nonsupervised learning is to uncover hidden structures or to represent the data in
Nonsupervised learning is valuable when labeled data is scarce, expensive to obtain, or when the underlying