What does "Labeled Data" mean?
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Labeled data is a type of information that has been marked with specific tags or categories. This helps computers understand what the data represents. For example, in a project that involves pictures of animals, a labeled image might be tagged as "dog," "cat," or "bird." This way, when a computer looks at the image, it knows what animal is in it.
Importance of Labeled Data
Labeled data is essential for training machine learning models. These models learn by looking at examples that are already categorized, which helps them make predictions or decisions about new data. The quality and quantity of labeled data can greatly influence how well the model performs.
Challenges with Labeled Data
Obtaining labeled data can be difficult and time-consuming. It often requires experts to look at the data and assign the correct tags. This can lead to situations where there is not enough labeled data available for training, which may hinder the model's ability to learn effectively.
Using Unlabeled Data
In recent methods, researchers look for ways to use unlabeled data, which is data that hasn't been tagged, along with labeled data. By applying different techniques, they can still train models effectively even when labeled examples are limited. This approach helps optimize the learning process and ensures that valuable information is not wasted.