CrossValidationTModel, TLearner, TInput, TOutput 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 array of data set indexes contained in each fold.

Gets the array of fold indices for each point in the data set.

Gets the number of folds in the k-fold cross validation.

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 cancellation token that can be used to cancel the algorithm while it is running.