BootstrapTModel, TInput Properties

Gets or sets the number B of bootstrap samplings to be drawn from the population dataset.

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 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 number of samples to be drawn in each subsample. If set to zero, all samples in the entire dataset will be selected.

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 the bootstrap samples drawn from the population dataset as indices.

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