Validation error - Validation error is the average loss a trained model incurs on a validation dataset that is separate from the examples used to fit it.
Validation loss - Validation loss is the value of a specified loss function measured on validation data for a model checkpoint or candidate.
Validation set - A validation set is a collection of examples used to assess candidate models during development, separately from the examples used to fit each candidate.
Variable importances - Variable importances are scores that describe how input variables matter to a fitted model according to a particular assessment method.
Verification and validation - Verification and validation, often abbreviated V&V, are complementary activities for assessing a system.
Wasserstein loss - Wasserstein loss is an objective based on a Wasserstein distance between probability distributions.
Z-score - A Z-score, or standard score, expresses how far a value lies above or below a reference mean in units of the reference standard deviation.
z-score normalization - Z-score normalization is a transformation that subtracts a reference mean from each numerical value and divides by a reference standard deviation.