Accord.MachineLearning.Performance Namespace
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Accord.MachineLearning.Performance Namespace

 
Classes
  ClassDescription
Public classBaseGridSearchTResult, TModel, TRange, TParam, TLearner, TInput, TOutput
Public classBaseSplitSetValidationTResult, TModel, TInput, TOutput
Public classBaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput
Public classCode exampleBootstrapTModel, TInput
Bootstrap method for generalization performance measurements (with support for stratification and default loss function for classification).
Public classCode exampleBootstrapTModel, TInput, TOutput
Bootstrap method for generalization performance measurements.
Public classBootstrapResultTModel, TInput, TOutput
Bootstrap validation analysis results.
Public classCode exampleCrossValidationTModel, TInput
k-Fold cross-validation (with support for stratification and default loss function for classification).
Public classCode exampleCrossValidationTModel, TInput, TOutput
k-Fold cross-validation.
Public classCode exampleCrossValidationTModel, TLearner, TInput, TOutput
k-Fold cross-validation.
Public classCode exampleCrossValidationResultTModel, TInput, TOutput
Class for representing results acquired through a k-fold cross-validation analysis.
Public classDataSubsetTInput
Subset of a larger dataset.
Public classDataSubsetTInput, TOutput
Subset of a larger dataset.
Public classCode exampleGridSearch
Grid search procedure for automatic parameter tuning.
Public classCode exampleGridSearchTInput, TOutput
Grid search procedure for automatic parameter tuning.
Public classCode exampleGridSearchTModel, TInput, TOutput
Grid search procedure for automatic parameter tuning.
Public classCode exampleGridSearchTModel, TLearner, TInput, TOutput
Grid search procedure for automatic parameter tuning.
Public classCode exampleGridSearchTModel, TRange, TLearner, TInput, TOutput
Grid search procedure for automatic parameter tuning.
Public classGridSearchRangeT
Range of parameters to be tested in a grid search.
Public classGridSearchResultTModel, TInput, TOutput
Contains results from the grid-search procedure.
Public classGridSearchResultTModel, TParam, TInput, TOutput
Contains results from the grid-search procedure.
Public classSetResultTModel
Training and validation errors of a model.
Public classSplitResultTModel, TInput, TOutput
Information class to store the training and validation errors of a model.
Public classCode exampleSplitSetValidationTModel, TInput
Split-Set Validation (with support for stratification and default loss function for classification).
Public classCode exampleSplitSetValidationTModel, TInput, TOutput
Split-Set Validation.
Public classTrainTestDataSplitTInput, TOutput
Training-Validation-Testing data split.
Public classTrainTestSplitT
Training-Test split.
Public classTrainValDataSplitTInput, TOutput
Training-Validation-Testing data split.
Public classTrainValSplitT
Training-Validation split.
Public classTrainValTestDataSplitTInput, TOutput
Training-Validation-Testing data split.
Public classTrainValTestSplitT
Training-Validation-Test split.
Interfaces
Delegates
  DelegateDescription
Public delegateComputeLossTOutput, TInfo
Function signature for a function that can compute a performance metric (i.e. a ILossT) from a set of expected (ground-truth) and actual (model prediction) output values. Additional information about the metric (such as its variance) or the learning problem (such as the expected number of classes) can be set in the object passed as the info parameter.
Public delegateCreateLearnerFromParameterTLearner, TParam
Function signature for a function that creates a machine learning model given a set of parameter values. This function should use the parameters to create and configure a ISupervisedLearningTModel, TInput, TOutput learning algorithm that can in turn be used to create a new machine learning model with those parameters.
Public delegateCreateLearnerFromSubsetTLearner, TInput, TOutput
Public delegateLearnNewModelTLearner, TInput, TOutput, TModel
Function signature for a function that specifies how a teacher should be used to create a new TModel.