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