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Residual loss refers to the difference between the actual output of a model and the output predicted by the model after accounting for the expected performance. It represents the portion of the loss that remains after the model has learned the underlying patterns in the data. In a statistical context, it quantifies the variability in the response variable that cannot be explained by the predictors used in the model. Understanding residual loss helps in assessing the model's effectiveness and identifying areas for improvement.

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21h ago

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