Latu.ML.TrainValidationSplitModel (latu_ml v0.2.0)

What a train/validation split found: the winning model, and one metric per param map.

validation_metrics is a single score per param map rather than a mean over folds, which is the only real difference from Latu.ML.CrossValidatorModel — there is nothing to average, so there is no standard deviation either.

best_model is refit on the whole frame, and sub_models is nil unless the search was asked to collect_sub_models: true. Latu.ML.delete/1 releases everything this holds.

Summary

Types

t()

Types

t()

@type t() :: %Latu.ML.TrainValidationSplitModel{
  best_model: Latu.ML.CrossValidatorModel.fitted(),
  session: Latu.Session.t(),
  sub_models: [Latu.ML.CrossValidatorModel.fitted()] | nil,
  uid: String.t(),
  validation_metrics: [float()],
  validator: Latu.ML.TrainValidationSplit.t()
}