Etiköinti

Etiköinti, often translated as "labeling" or "tagging," is a fundamental process in data science and machine learning. It involves assigning predefined categories or labels to raw data. This process is crucial for supervised learning algorithms, where the algorithm learns to make predictions based on labeled examples. For instance, in image recognition,etiköinti would involve drawing bounding boxes around objects and assigning them a class label like "cat" or "dog." In natural language processing, it could mean tagging words with their part of speech (noun, verb, adjective) or identifying named entities such as people, organizations, and locations. The quality of the etiköinti directly impacts the performance of the machine learning model. Inaccurate or inconsistent labels can lead to a model that makes incorrect predictions. Therefore, careful planning, clear guidelines, and often human annotators are employed to ensure high-quality etiköinti. The process can be time-consuming and expensive, especially for large datasets, leading to ongoing research in semi-supervised and unsupervised learning methods that require less manual labeling. The term can also refer to the act of placing a physical label on a product for identification or marketing purposes, though in a technical context, it almost exclusively refers to data annotation.