// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Label
The target variable or output that a machine learning model is trying to predict, often a category or a numerical value.
TECHNICAL DEFINITION
A Label, in supervised learning, refers to the ground truth output or target variable associated with an input feature vector, serving as the correct answer that the model learns to predict during training and is evaluated against during testing.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Target
- Output
- Class
- Dependent Variable
- Response Variable
USAGE NOTE
Essential for supervised learning algorithms, which learn mappings from input features to these known outputs.
DEVELOPERS
Organizations developing technology related to Label.
Provides a data labeling platform and services for training AI applications, including data annotation for various modalities like text, images, and audio, essential for AI engineering and model training.
A global leader in data for the AI lifecycle, offering data collection and annotation services to train and improve AI and machine learning models, supporting prompt design and AI development.
An enterprise-grade data labeling platform that provides tools for annotating data, managing datasets, and collaborating on machine learning projects, crucial for AI engineering workflows.
Develops a programmatic data labeling platform that allows AI engineers to quickly label, build, and manage training data using code, emphasizing a data-centric approach to AI.
Offers data collection, annotation, and evaluation services for a wide range of AI applications, including natural language processing and computer vision, supporting the data needs of AI engineers.
Provides an end-to-end platform for data annotation and data management, accelerating the development of computer vision and natural language processing models for AI engineers.
A crowdsourcing marketplace that enables businesses to outsource data labeling and human intelligence tasks (HITs) to a distributed workforce, widely used for generating labeled datasets for AI.
Offers managed dataset labeling services as part of its unified machine learning platform, allowing users to get human-labeled data for training their AI and ML models efficiently.