Definition
This critical step involves attaching meaningful metadata to raw data points so that algorithms can learn the relationship between input and output. For example, bounding boxes around objects in images or sentiment labels for text reviews. High-quality annotation is essential for the performance of supervised learning models, as the model’s ability to generalize depends directly on the accuracy and consistency of these labels.
Summary
Data annotation is the process of labeling raw data, such as images or text, to make it suitable for supervised machine learning training.
Key Concepts
- Supervised Learning
- Labeling
- Ground Truth
- Metadata
Use Cases
- Training object detection models
- Building sentiment analysis classifiers
- Creating speech recognition datasets