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Ground Truth
The correct or true labels/values for data, used as targets during training and evaluation benchmarks.
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A collection of data examples used for training, validating, or testing machine learning models.
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Foundations Labeled Data Data with associated target outputs or annotations, required for supervised learning tasks. 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. Evaluation Accuracy The proportion of correct predictions out of total predictions, a basic classification metric. Evaluation Test Set A final portion of data unseen during training and validation, used for unbiased evaluation of model performance. Foundations Supervised Learning Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.