# lsvmnode properties #

![LSVM node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/lsvm_icon.png)With the Linear Support Vector Machine (LSVM) node, you can classify data into one of two groups without overfitting\. LSVM is linear and works well with wide data sets, such as those with a very large number of records\.

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lsvmnode properties  

Table 1\. lsvmnode properties

| `lsvmnode` Properties           | Values                  | Property description                                                                                                                                                                                                                                                                                                                                                                                                         |
| ------------------------------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `intercept`                     | *flag*                  | Includes the intercept in the model\. Default value is `True`\.                                                                                                                                                                                                                                                                                                                                                              |
| `target_order`                  | `Ascending``Descending` | Specifies the sorting order for the categorical target\. Ignored for continuous targets\. Default is `Ascending`\.                                                                                                                                                                                                                                                                                                           |
| `precision`                     | *number*                | Used only if measurement level of target field is `Continuous`\. Specifies the parameter related to the sensitiveness of the loss for regression\. Minimum is `0` and there is no maximum\. Default value is `0.1`\.                                                                                                                                                                                                         |
| `exclude_missing_values`        | *flag*                  | When `True`, a record is excluded if any single value is missing\. The default value is `False`\.                                                                                                                                                                                                                                                                                                                            |
| `penalty_function`              | `L1``L2`                | Specifies the type of penalty function used\. The default value is `L2`\.                                                                                                                                                                                                                                                                                                                                                    |
| `lambda`                        | *number*                | Penalty (regularization) parameter\.                                                                                                                                                                                                                                                                                                                                                                                         |
| `calculate_variable_importance` | *flag*                  | For models that produce an appropriate measure of importance, this option displays a chart that indicates the relative importance of each predictor in estimating the model\. Note that variable importance may take longer to calculate for some models, particularly when working with large datasets, and is off by default for some models as a result\. Variable importance is not available for decision list models\. |

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