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Labeled Data
Data with associated target outputs or annotations, required for supervised learning tasks.
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A collection of data examples used for training, validating, or testing machine learning models.
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Foundations Supervised Learning Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen. Foundations Ground Truth The correct or true labels/values for data, used as targets during training and evaluation benchmarks. Foundations Semi-Supervised Learning Learning from a combination of labeled and unlabeled data, leveraging abundant unlabeled data to improve performance. Foundations Active Learning Iteratively selecting the most informative unlabeled examples for annotation to efficiently improve models with limited labels. Foundations Training Data The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.