What Labeling Meaning, Applications & Example
The process of assigning labels to data for supervised learning.
What is Labeling?
Labeling refers to the process of assigning a specific tag, category, or value to a data point, usually in supervised machine learning tasks. It is a critical step in preparing data for training algorithms, allowing models to learn patterns and make predictions based on labeled examples.
Types of Labeling
- Manual Labeling: Human annotators assign labels to data, ensuring high accuracy but often being time-consuming and expensive.
- Automated Labeling: Uses algorithms to assign labels, typically less accurate but more efficient for large datasets.
- Semi-supervised Labeling: Combines a small amount of manually labeled data with a larger pool of unlabeled data, using algorithms to propagate labels.
- Active Labeling: Involves an interactive process where the algorithm selects the most uncertain or informative data points for labeling.
Applications of Labeling
- Image Classification: Labels images with categories (e.g., “dog,” “cat”) to train a model to recognize objects.
- Sentiment Analysis : Labels text data as positive, negative, or neutral to train models for opinion mining.
- Medical Imaging: Labels images like X-rays or MRIs to identify diseases or conditions.
- Speech Recognition: Labels audio data with transcriptions or commands to train speech-to-text models.
Example of Labeling
In image classification, labeling might involve tagging each image in a dataset with a label such as “dog,” “cat,” or “car.” This labeled dataset is then used to train a model that can predict the correct label for new, unseen images.