AML SVM Properties
- Updated2023-02-21
- 3 minute(s) read
Wire the untrained SVM model output of the Initialize Classification Model (SVM) VI to the reference input of a standard Property Node to get an AML SVM Property Node. This Property Node has the following properties:
| Property | Access | Data Type | Description |
|---|---|---|---|
| Coefficients of Support Vectors | Read Only |
|
Returns the coefficients of support vectors. |
| Cross Validation Configuration | Read Only |
|
Returns settings for cross validation. |
| Decision Function Constants | Read Only |
|
Returns the decision function constants. |
| Hyperparameters | Read Only |
|
If you select the Set Parameters instance the Initialize Classification Model (SVM) VI, this property returns the hyperparameters input of the VI. If you select the Search Parameters instance the Initialize Classification Model (SVM) VI, this property returns the optimal hyperparameters after the Train Classification Model VI completes grid search. |
| Hyperparameter Grids | Read Only |
|
Returns multiple values for each hyperparameter. |
| Hyperparameter Optimization | Read Only |
|
Returns the method of optimization to determine the optimal hyperparameter settings. |
| Label of Each Class | Read Only |
|
Returns the label of each class. |
| Indexes of Support Vectors | Read Only |
|
Returns the indexes of support vectors that you can use to find support vectors from the training data. |
| Model Initialized? | Read Only |
|
Returns whether the model is initialized. |
| Model Trained? | Read Only |
|
Returns whether the model is trained. |
| Number of Classes | Read Only |
|
Returns the number of classes. |
| Number of Support Vectors | Read Only |
|
Returns the number of support vectors. |
| Number of Support Vectors for Each Class | Read Only |
|
Returns the number of support vectors for each class. |
| Pairwise Probability | Read Only |
|
Returns the estimated probabilities of the classes in the trained SVM model. |
| Support Vectors | Read Only |
|
Returns all the support vectors. The number of rows equals the number of vectors and the number of columns equals the number of features. |
