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CrossValidationTModel, TInput Properties |
The CrossValidationTModel, TInput generic type exposes the following members.
Properties| Name | Description | |
|---|---|---|
 | DefaultValue |
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).
(Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.) |
| Fit |
Gets or sets a LearnNewModelTLearner, TInput, TOutput, TModel function that can be used to create
new machine learning models using the current
learning algorithm.
(Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.) |
| Folds |
Gets the array of data set indexes contained in each fold.
(Inherited from CrossValidationTModel, TLearner, TInput, TOutput.) |
| Indices |
Gets the array of fold indices for each point in the data set.
(Inherited from CrossValidationTModel, TLearner, TInput, TOutput.) |
| K |
Gets the number of folds in the k-fold cross validation.
(Inherited from CrossValidationTModel, TLearner, TInput, TOutput.) |
| Learner |
Gets or sets a CreateLearnerFromSubsetTLearner, TInput, TOutput function
that can be used to create a TModel from a subset of the learning dataset.
(Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.) |
| Loss |
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.
(Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.) |
| ParallelOptions |
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.) |
| Stratify |
Gets or sets a value indicating whether the prevalence of an output
label should be balanced between training and testing sets. Default is false.
|
| Token |
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.) |
See Also