Adatcímkézésben

Adatcímkézésben is a Hungarian term that translates to "in data labeling" or "with data labeling." It refers to the process of annotating or tagging raw data to make it understandable and usable for machine learning algorithms. This process is crucial for training supervised learning models, where the accuracy of the model heavily depends on the quality of the labeled data.

The purpose of data labeling is to provide context and meaning to unstructured or semi-structured data. For example, in image recognition, data labeling involves drawing bounding boxes around objects in an image and assigning labels to them, such as "car," "person," or "tree." In natural language processing, it can involve sentiment analysis, where text is labeled as positive, negative, or neutral, or named entity recognition, where specific entities like names, organizations, or locations are identified.

Data labeling can be performed manually by human annotators or through semi-automated methods that leverage machine learning to assist the labeling process. The choice of labeling method often depends on the complexity of the data, the required accuracy, and the available resources. Various tools and platforms exist to facilitate data labeling, offering features for managing datasets, assigning tasks, and ensuring quality control. The outcome of effective data labeling is a dataset that accurately represents the problem domain, enabling the development of robust and reliable machine learning models.