adatcímkézésnél

Adatcímkézésnél is a Hungarian term that translates to "data labeling" or "data annotation" in English. It refers to the process of identifying and tagging raw data, such as images, text, audio, or video, with meaningful labels or annotations. This labeled data is crucial for training and validating machine learning models, particularly in supervised learning algorithms.

The purpose of data labeling is to provide machines with the context and understanding necessary to perform specific tasks. For example, in computer vision, images might be labeled with bounding boxes around objects of interest, or pixel-level masks to delineate their exact shape. For text data, labels could indicate sentiment, identify named entities, or classify the topic of a document. Audio data might be transcribed or categorized by speaker.

The accuracy and quality of data labeling directly impact the performance of the resulting machine learning models. Inaccurate or inconsistent labels can lead to poor predictions and unreliable outcomes. Therefore, data labeling is often a labor-intensive process that requires human annotators with domain expertise and clear guidelines. Various tools and platforms exist to facilitate and manage the data labeling workflow, ensuring efficiency and consistency. The generated labeled datasets are fundamental for developing AI applications across diverse fields, including autonomous driving, medical imaging analysis, natural language processing, and content moderation.