Training error refers to the difference between the predicted outputs of a model and the actual outputs from the training dataset. It is a crucial measure of how well a model has learned from its training data. Understanding training error helps in assessing model performance and is directly linked to concepts such as overfitting and underfitting, which are important when discussing the bias-variance tradeoff.
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