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Train-Test Split
Dividing a dataset into separate portions for training the model and evaluating its performance on unseen data.
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A collection of data examples used for training, validating, or testing machine learning models.
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Evaluation Validation Set Data held back from training and used to choose hyperparameters and checkpoints, so those choices are judged on examples the model never fit. Evaluation Test Set Data locked away until the end of a project and used once to estimate how the finished model will perform on new, unseen examples. Evaluation Cross-Validation A technique for assessing model performance by partitioning data into subsets, training on some and validating on others. Evaluation Data Leakage When information from outside the training data is used to create the model, leading to overly optimistic performance estimates. Training Overfitting When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.