Labeling

Labeling is the process of assigning one or more meaningful and informative tags or labels to a dataset or a subset of it. The purpose of labeling is to provide a clear understanding of the data and to make it easier to analyze and interpret. Labeling is an essential step in supervised learning, where the labeled data is used to train machine learning models. The quality and accuracy of labeling directly affect the performance of the trained models. Labeling can be done manually or automatically using various techniques such as active learning, semi-supervised learning, and crowdsourcing. Manual labeling is time-consuming and expensive, but it provides high accuracy and quality. Automatic labeling is faster and cheaper, but it may not be as accurate as manual labeling. Labeling is widely used in various applications such as image recognition, natural language processing, and speech recognition.
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