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A test data set (also called a holdout set) is the portion of data you reserve until the very end of model development to evaluate how well your final model will perform on new, unseen data. Here’s how the pieces work together: \t•\tTraining data is used to fit the model—the algorithm learns patterns by adjusting its internal parameters to reduce error on the training examples. \t•\tValidation data is used to improve generalization—you compare model versions and tune choices like features, model
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