training residuals vs validation residuals

Both 'training residuals' and 'validation residuals' are correct and commonly used terms in the context of statistical modeling. 'Training residuals' refer to the differences between the observed and predicted values in the training dataset, while 'validation residuals' refer to the differences in the validation dataset. The choice between the two depends on the specific phase of the modeling process being referred to.

Last updated: March 17, 2024

training residuals

This phrase is correct and commonly used in statistical modeling.

This term refers to the differences between the observed values and the values predicted by a model using the training dataset. It is used to assess the model's performance during the training phase.

Examples:

The analyst examined the training residuals to evaluate the model's fit to the data.

Alternatives:

residuals from the training set
model residuals from training data
training error residuals
training set residuals
residuals in the training phase

validation residuals

This phrase is correct and commonly used in statistical modeling.

This term refers to the differences between the observed values and the values predicted by a model using the validation dataset. It is used to assess the model's performance on unseen data.

Examples:

The researcher analyzed the validation residuals to determine the model's generalization ability.

Alternatives:

residuals from the validation set
model residuals from validation data
validation error residuals
validation set residuals
residuals in the validation phase

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training residuals vs validation residuals