Data Labeling refers to the process of annotating or tagging data with meaningful information to make it understandable for machine learning models. This step is crucial for supervised learning, where models learn from labeled examples to make predictions or decisions.
Characteristics:
– Involves adding tags or metadata to raw data such as images, text, audio, or video.
– Can be done manually by human annotators or automatically using software tools.
– Ensures data quality and accuracy, which directly impacts model performance.
– Supports various AI tasks like image recognition, natural language processing, and speech recognition.
Examples:
– Labeling images with tags like “cat,” “dog,” or “car” for an object detection model.
– Annotating customer reviews as “positive,” “negative,” or “neutral” for sentiment analysis.
– Tagging audio clips with speaker identity or transcriptions for speech recognition systems.
