# ocsvmnode properties #

![One\-Class SVM node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/pythononeclassnodeicon.png)The One\-Class SVM node uses an unsupervised learning algorithm\. The node can be used for novelty detection\. It will detect the soft boundary of a given set of samples, to then classify new points as belonging to that set or not\. This One\-Class SVM modeling node in SPSS Modeler is implemented in Python and requires the  scikit\-learn© Python library\.

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ocsvmnode properties  

Table 1\. ocsvmnode properties

| `ocsvmnode` properties | Data type | Property description                                                                                                                                                                                                                                                                                                         |
| ---------------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `custom_fields`        | *boolean* | This option tells the node to use field information specified here instead of that given in any upstream Type node(s)\. After selecting this option, specify the following fields as required\.                                                                                                                              |
| `inputs`               | *field*   | List of the field names for input\.                                                                                                                                                                                                                                                                                          |
| `role_use`             | *string*  | Specify `predefined` to use predefined roles or `custom` to use custom field assignments\. Default is predefined\.                                                                                                                                                                                                           |
| `splits`               | *field*   | List of the field names for split\.                                                                                                                                                                                                                                                                                          |
| `use_partition`        | *Boolean* | Specify `true` or `false`\. Default is `true`\. If set to `true`, only training data will be used when building the model\.                                                                                                                                                                                                  |
| `mode_type`            | *string*  | The mode\. Possible values are `simple` or `expert`\. All parameters on the Expert tab will be disabled if `simple` is specified\.                                                                                                                                                                                           |
| `stopping_criteria`    | *string*  | A string of scientific notation\. Possible values are `1.0E-1`, `1.0E-2`, `1.0E-3`, `1.0E-4`, `1.0E-5`, or `1.0E-6`\. Default is `1.0E-3`\.                                                                                                                                                                                  |
| `precision`            | *float*   | The regression precision (nu)\. Bound on the fraction of training errors and support vectors\. Specify a number greater than `0` and less than or equal to `1.0`\. Default is `0.1`\.                                                                                                                                        |
| `kernel`               | *string*  | The kernel type to use in the algorithm\. Possible values are `linear`, `poly`, `rbf`, `sigmoid`, or `precomputed`\. Default is `rbf`\.                                                                                                                                                                                      |
| `enable_gamma`         | *Boolean* | Enables the `gamma` parameter\. Specify `true` or `false`\. Default is `true`\.                                                                                                                                                                                                                                              |
| `gamma`                | *float*   | This parameter is only enabled for the kernels `rbf`, `poly`, and `sigmoid`\. If the `enable_gamma` parameter is set to `false`, this parameter will be set to `auto`\. If set to `true`, the default is `0.1`\.                                                                                                             |
| `coef0`                | *float*   | Independent term in the kernel function\. This parameter is only enabled for the `poly` kernel and the `sigmoid` kernel\. Default value is `0.0`\.                                                                                                                                                                           |
| `degree`               | *integer* | Degree of the polynomial kernel function\. This parameter is only enabled for the `poly` kernel\. Specify any integer\. Default is `3`\.                                                                                                                                                                                     |
| `shrinking`            | *Boolean* | Specifies whether to use the shrinking heuristic option\. Specify `true` or `false`\. Default is `false`\.                                                                                                                                                                                                                   |
| `enable_cache_size`    | *Boolean* | Enables the `cache_size` parameter\. Specify `true` or `false`\. Default is `false`\.                                                                                                                                                                                                                                        |
| `cache_size`           | *float*   | The size of the kernel cache in MB\. Default is `200`\.                                                                                                                                                                                                                                                                      |
| `enable_random_seed`   | *Boolean* | Enables the `random_seed` parameter\. Specify `true` or `false`\. Default is `false`\.                                                                                                                                                                                                                                       |
| `random_seed`          | *integer* | The random number seed to use when shuffling data for probability estimation\. Specify any integer\.                                                                                                                                                                                                                         |
| `pc_type`              | *string*  | The type of the parallel coordinates graphic\. Possible options are `independent` or `general`\.                                                                                                                                                                                                                             |
| `lines_amount`         | *integer* | Maximum number of lines to include on the graphic\. Specify an integer between `1` and `1000`\.                                                                                                                                                                                                                              |
| `lines_fields_custom`  | *Boolean* | Enables the `lines_fields` parameter, which allows you to specify custom fields to show in the graph output\. If set to `false`, all fields will be shown\. If set to `true`, only the fields specified with the lines\_fields parameter will be shown\. For performance reasons, a maximum of 20 fields will be displayed\. |
| `lines_fields`         | *field*   | List of the field names to include on the graphic as vertical axes\.                                                                                                                                                                                                                                                         |
| `enable_graphic`       | *Boolean* | Specify `true` or `false`\. Enables graphic output (disable this option if you want to save time and reduce stream file size)\.                                                                                                                                                                                              |
| `enable_hpo`           | *Boolean* | Specify `true` or `false` to enable or disable the HPO options\. If set to `true`, Rbfopt will be applied to find out the "best" One\-Class SVM model automatically, which reaches the target objective value defined by the user with the following `target_objval` parameter\.                                             |
| `target_objval`        | *float*   | The objective function value (error rate of the model on the samples) we want to reach (for example, the value of the unknown optimum)\. Set this parameter to the appropriate value if the optimum is unknown (for example, `0.01`)\.                                                                                       |
| `max_iterations`       | *integer* | Maximum number of iterations for trying the model\. Default is `1000`\.                                                                                                                                                                                                                                                      |
| `max_evaluations`      | *integer* | Maximum number of function evaluations for trying the model, where the focus is accuracy over speed\. Default is `300`\.                                                                                                                                                                                                     |

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