What is labeling?
Labeling is a vital part of training AI. It allows AI to understand the implications of a pattern. Once an AI knows the label associated with a pattern, it will evaluate future patterns based on that knowledge. When you like a song, you are helping the algorithm find other songs that share some of the same features. If you continue to like the next set of songs, the music app becomes even more precise in finding similar songs.
Why is labeling important?
Data labeling enables machines to gain an accurate understanding of real-world conditions and opens opportunities for a wide range of businesses and industries. It is critical to achieving that potential, like having better-labeled data than competitors provides superior ML industry. Data labeling is an integral part of data preprocessing for ML, particularly for supervised learning. Both input and output data are labeled for classification to provide a learning basis for future data processing.
Labeled datasets help train ML models to identify and understand the recurring patterns in the input fed into them to deliver accurate output. After being trained by annotated data, ML models can recognize the same patterns in the new unstructured data. Massive amounts of data are required to train and fine-tune the various ML models. But the data must be in a structured and labeled form to be used during the iterative process of testing and validating ML models.