| Application
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Returns application object. Return value: Object. This property is read only.
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| ApplyCrossV
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Select this option to apply v-fold cross-validation. Return/assignment value: Integer.
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| ApplySampling
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Divides the data set into training and test samples. Return/assignment value: Integer.
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| CParameter
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Capacity parameter (for Classification Type 1, Regression Type 1, and Regression Type 2 SVM models). Return/assignment value: Double.
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| CacheSize
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Specifies the cache size. Return/assignment value: Integer.
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| CasewiseDeletionOfMD
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Casewise deletion of missing data (MD). Return/assignment value: Integer.
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| ClassType1
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Classification SVM type 1. Return/assignment value: Integer.
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| ClassType2
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Classification SVM type 2. Return/assignment value: Integer.
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| CodeForTrainingSample
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Code for training sample. Return/assignment value: String.
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| Coefficient
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Coefficient parameter (for polynomial and sigmoid kernels). Return/assignment value: Double.
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| CrossVSeed
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Specifies the random number generator seed to be used in the process of (randomly) grouping the data into v folds. Return/assignment value: Integer.
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| Degree
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Degree parameter (for polynomial kernels). Return/assignment value: Integer.
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| DeltaC
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Specifies the increase in the value of the capacity parameter when performing the cross-validation grid search. Return/assignment value: Double.
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| DeltaEpsilon
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Specifies the increase in the value of the epsilon parameter when performing the cross-validation grid search. Return/assignment value: Double.
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| DeltaNu
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Specifies the increase in the value of the nu parameter when performing the cross-validation grid search. Return/assignment value: Double.
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| EpsilonParameter
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Epsilon parameter (for Regression SVM Type 1). Return/assignment value: Double.
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| Gam
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Gamma parameter (for polynomial, RBF and sigmoid kernels). Return/assignment value: Double.
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| LinearKernel
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Linear kernel. Return/assignment value: Integer.
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| MaxNoOfIterations
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Maximum number of iterations for SVM training. Return/assignment value: Integer.
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| MaximumC
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Specifies the maximum value of the capacity parameter to start with in the cross-validation grid search. Return/assignment value: Double.
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| MaximumEpsilon
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Specifies the maximum value of the epsilon parameter to start with in the cross-validation grid search. Return/assignment value: Double.
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| MaximumNu
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Specifies the maximum value of the nu parameters to start with in the cross-validation grid search. Return/assignment value: Double.
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| MeanSubstitutionOfMD
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Mean substitution of missing data (MD). Return/assignment value: Integer.
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| MinimumC
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Specifies the minimum value of the capacity parameter to start with in the cross-validation grid search. Return/assignment value: Double.
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| MinimumEpsilon
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Specifies the minimum value of the epsilon parameter to start with in the cross-validation grid search. Return/assignment value: Double.
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| MinimumNu
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Specifies the minimum value of the nu parameters to start with in the cross-validation grid search. Return/assignment value: Double.
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| NValue
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The number of first N cases to be used as the training sample. Return/assignment value: Integer.
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| Name
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Return value: String. This property is read only.
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| NuParameter
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Nu parameter (for Classification Type 2 and Regression SVM Type 2). Return/assignment value: Double.
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| Parent
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Returns the parent of the object. Return value: Object. This property is read only.
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| PenaltyList
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Enter individual numbers separated by spaces (e.g. 1 2 5) to define class penalties for imbalanced data. Return/assignment value: String.
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| PolyKernel
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Polynomial kernel. Return/assignment value: Integer.
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| PredictorCodes
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Predictor codes. Return/assignment value: String.
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| RBFKernel
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Radial Basis Function (RBF) kernel. Return/assignment value: Integer.
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| RegressionType1
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Regression SVM type 1. Return/assignment value: Integer.
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| RegressionType2
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Regression SVM type 2. Return/assignment value: Integer.
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| ResponseCodes
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Response codes. Return/assignment value: String.
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| ResultsCode
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Assignment value: Variant.
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| ResultsOption
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Assignment value: Integer.
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| ResultsOutputFields
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Assignment value: Variant.
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| ResultsSaveFileName
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Assignment value: String.
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| ResultsSelection
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Assignment value: Variant.
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| ResultsSelectionOption
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Assignment value: Variant.
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| ResultsValues
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Assignment value: Variant.
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| ResultsVariables
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Assignment value: Variant.
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| SamplingVariable
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Specifies the sample variable for dividing the data set into train and testing samples. Return/assignment value: String.
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| ScaleInputs
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Scale continuous inputs. Return/assignment value: Integer.
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| ScaleOutputs
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Scale continuous outputs. Return/assignment value: Integer.
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| SeedForRandomSampling
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Specifies the random number generator seed to be used in the process of (randomly) dividing the data into train and test samples. Return/assignment value: Integer.
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| ShrinkData
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Shrinks data for computational efficiency. Return/assignment value: Integer.
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| SigmoidKernel
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Sigmoid kernel. Return/assignment value: Integer.
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| SizeOfTrainingSample
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Specifies the percentage of the valid cases (determined by case selection conditions) in the data set that will be used to form the training set. Return/assignment value: Double.
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| StoppingAccuracy
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Specifies the target error at which training is stopped. Return/assignment value: Double.
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| UseFirstNCases
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Selects the first N valid cases [determined by the case selection conditions (see case selections)] of the data set as the training sample. The remaining cases will be used for testing. Return/assignment value: Integer.
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| UsePenalty
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Select whether to apply class penalty for imbalanced data. Return/assignment value: Integer.
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| UseRandomSampling
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Generates the training and test samples randomly. Return/assignment value: Integer.
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| UseSubsetvariable
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Generates the training and test samples from a subsample (subset) variable. Return/assignment value: Integer.
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| VValue
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Specifies the number of folds used to perform the cross-validation. Return/assignment value: Integer.
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| Variables
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Select variables for the analysis. Return/assignment value: String.
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