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SplitSetValidationTModel, TInput, TOutput Class

Split-Set Validation.
Inheritance Hierarchy
SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearning.PerformanceBaseSplitSetValidationSplitResultTModel, TInput, TOutput, TModel, ISupervisedLearningTModel, TInput, TOutput, TInput, TOutput
      Accord.MachineLearning.PerformanceBaseSplitSetValidationSplitResultTModel, TInput, TOutput, TModel, TInput, TOutput
        Accord.MachineLearning.PerformanceSplitSetValidationTModel, TInput, TOutput
          Accord.MachineLearning.PerformanceSplitSetValidationTModel, TInput

Namespace:  Accord.MachineLearning.Performance
Assembly:  Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0
Syntax
public class SplitSetValidation<TModel, TInput, TOutput> : BaseSplitSetValidation<SplitResult<TModel, TInput, TOutput>, TModel, TInput, TOutput>
where TModel : class, Object, ITransform<TInput, TOutput>
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Type Parameters

TModel
The type of the machine learning model.
TInput
The type of the input data.
TOutput
The type of the output data or labels.

The SplitSetValidationTModel, TInput, TOutput type exposes the following members.

Constructors
  NameDescription
Public methodSplitSetValidationTModel, TInput, TOutput
Initializes a new instance of the SplitSetValidationTModel, TInput, TOutput class.
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Properties
  NameDescription
Public propertyDefaultValue
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.)
Public propertyFit (Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
Public propertyIndices
Gets the group labels assigned to each of the data samples.
Public propertyIndicesTrainingSet
Gets the indices of elements in the training set.
Public propertyIndicesValidationSet
Gets the indices of elements in the validation set.
Public propertyLearner (Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
Public propertyLoss
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.)
Public propertyParallelOptions
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)
Public propertyToken
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)
Public propertyTrainingSetProportion
Gets or sets the proportion of samples that should be reserved in the training set. Default is 80%.
Public propertyValidationSetProportion
Gets or sets the proportion of samples that should be reserved in the validation set. Default is 20%.
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Methods
  NameDescription
Protected methodCreateValidationSplits
Creates a list of the sample indices that should serve as the validation set.
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodLearn
Learns a model that can map the given inputs to the given outputs.
(Overrides BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutputLearn(TInput, TOutput, Double).)
Protected methodLearnSubset
Learns and evaluates a model in a single subset of the data.
(Inherited from BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(Type)Overloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
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Examples
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;

// This is a sample code on how to use Train-Val validation (split-set)
// to assess the performance of Support Vector Machines.

// Consider the example binary data. We will be trying to learn a XOR 
// problem and see how well does SVMs perform on this data.

double[][] data =
{
    new double[] { -1, -1 }, new double[] {  1, -1 },
    new double[] { -1,  1 }, new double[] {  1,  1 },
    new double[] { -1, -1 }, new double[] {  1, -1 },
    new double[] { -1,  1 }, new double[] {  1,  1 },
    new double[] { -1, -1 }, new double[] {  1, -1 },
    new double[] { -1,  1 }, new double[] {  1,  1 },
    new double[] { -1, -1 }, new double[] {  1, -1 },
    new double[] { -1,  1 }, new double[] {  1,  1 },
};

int[] xor = // result of xor for the sample input data
{
    -1,       1,
     1,      -1,
    -1,       1,
     1,      -1,
    -1,       1,
     1,      -1,
    -1,       1,
     1,      -1,
};


// Create a new Cross-validation algorithm passing the data set size and the number of folds
var splitset = new SplitSetValidation<SupportVectorMachine<Linear, double[]>, double[]>()
{
    Learner = (s) => new SequentialMinimalOptimization<Linear, double[]>()
    {
        Complexity = 1000
    },

    Loss = (expected, actual, p) => new ZeroOneLoss(expected).Loss(actual),

    Stratify = false,
};

splitset.ParallelOptions.MaxDegreeOfParallelism = 1;

// Compute the cross-validation
var result = splitset.Learn(data, xor);

// Finally, access the measured performance.
double trainingErrors = result.Training.Value; // should be 0.53846153846153844 (+/- var. 0)
double validationErrors = result.Validation.Value; // should be 0.33333333333333331 (+/- var. 0)
See Also