Labeled Data: Insights & Accurate ML Models

Glossary/L/Labeled Data

What is Labeled Data?

Labeled data refers to a dataset where each example or instance is associated with a specific label or category. Labeled data is essential for training and evaluating generative models that aim to generate new instances or samples that align with the provided labels.

Labeled data serves as a reference or ground truth for the model to learn the underlying patterns and distributions of the data. The labels provide explicit information about the characteristics or properties of the instances, allowing the generative model to understand and capture the relationships between the input features and their corresponding labels.

Related terms

Probablistic Modeling

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Generative AI

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Not to be confused with:

Probablistic Modeling

Probabilistic modeling is a powerful approach in machine learning and statistics that enables us to ...

Generative AI

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating AI systems...

Probablistic Modeling

Probabilistic modeling is a powerful approach in machine learning and statistics that enables us to ...

Generative AI

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating AI systems...

Probablistic Modeling

Probabilistic modeling is a powerful approach in machine learning and statistics that enables us to ...

Generative AI

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating AI systems...

For example, in text generation, labeled data could consist of pairs of input texts and their corresponding categories or sentiment labels. By training a generative model on such labeled data, the model can learn to generate new texts that exhibit the desired sentiment or category based on the learned associations.

Labeled data plays a crucial role in the training process of generative models. During training, the model is presented with labeled examples, and it learns to map the input features to the corresponding labels. The model optimizes its parameters based on the discrepancy between its predicted output and the true labels, typically using techniques such as maximum likelihood estimation or other probabilistic frameworks.

In addition to training, labeled data is also valuable for evaluating the performance of generative models. By comparing the generated samples to the true labels, various evaluation metrics can be employed to assess the quality, diversity, or fidelity of the generated output. Labeled data enables the calculation of metrics such as accuracy, precision, recall, or measures specific to the generative task at hand.

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