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SplitSetValidationTModel, TInput, TOutput Properties |
The SplitSetValidationTModel, TInput, TOutput 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.) |
| Indices |
Gets the group labels assigned to each of the data samples.
|
| IndicesTrainingSet |
Gets the indices of elements in the training set.
|
| IndicesValidationSet |
Gets the indices of elements in the validation set.
|
| 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.) |
| Token |
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.) |
| TrainingSetProportion |
Gets or sets the proportion of samples that should be
reserved in the training set. Default is 80%.
|
| ValidationSetProportion |
Gets or sets the proportion of samples that should be
reserved in the validation set. Default is 20%.
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See Also