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Validation Loss

Validation loss is a measure of error between the predicted outcomes by a machine learning model and the actual outcomes of a validation dataset. It is a key metric for evaluating a model's performance and for tuning hyperparameters.

Understanding Validation Loss

Unlike training loss, which is calculated during the model training phase, validation loss is computed on a separate, unseen dataset. It helps in determining how well a model generalizes to new data. A model with low training loss but high validation loss is likely overfitting.

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