SplitSetValidationTModel, TInput Properties

Gets or sets a value to be used as the Loss in case the model throws an exception during learning. Default is null (exceptions will not be ignored).

Gets or sets a LearnNewModelTLearner, TInput, TOutput, TModel function that can be used to create new machine learning models using the current learning algorithm.

Gets the group labels assigned to each of the data samples.

Gets the indices of elements in the training set.

Gets the indices of elements in the validation set.

Gets or sets a CreateLearnerFromSubsetTLearner, TInput, TOutput function that can be used to create a TModel from a subset of the learning dataset.

Gets or sets a ComputeLossTOutput, TInfo function that can be used to measure how far the actual model predictions were from the expected ground-truth.

Gets or sets the parallelization options for this algorithm.

Gets or sets a value indicating whether the prevalence of an output label should be balanced between training and testing sets. Default is false.

Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.

Gets or sets the proportion of samples that should be reserved in the training set. Default is 80%.

Gets or sets the proportion of samples that should be reserved in the validation set. Default is 20%.