Unlabeled Data
Unlabeled Data is data that does not have predefined labels, categories, or target values identifying the expected outcome for each example.
What is Unlabeled Data?
Unlabeled data can contain raw information such as text, images, audio, or numerical records without annotations indicating what each example represents. Machine learning systems can analyze this data to discover patterns, relationships, or useful representations without relying on manually assigned labels.
Why is Unlabeled Data Important?
Unlabeled data is often much easier and less expensive to collect than labeled data. It can provide large amounts of information for training AI systems, particularly in unsupervised and semi-supervised learning, where models can learn useful patterns without requiring every example to be manually labeled.
Common use cases
Unlabeled Data is commonly used in clustering, representation learning, self-supervised learning, semi-supervised learning, anomaly detection, and large-scale AI model training.