# bayesnetnode properties #

![Bayes Net node icon](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/images/bayesian_network_icon.png)With the Bayesian Network (Bayes Net) node, you can build a probability model by combining observed and recorded evidence with real\-world knowledge to establish the likelihood of occurrences\. The node focuses on Tree Augmented Naïve Bayes (TAN) and Markov Blanket networks that are primarily used for classification\.

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bayesnetnode properties  

Table 1\. bayesnetnode properties

| `bayesnetnode` Properties          | Values                     | Property description                                                                                                                                                                                    |
| ---------------------------------- | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `inputs`                           | *\[field1 \.\.\. fieldN\]* | Bayesian network models use a single target field, and one or more input fields\. Continuous fields are automatically binned\. See the topic [Common modeling node properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/modelingnodeslots_common.html#modelingnodeslots_common) for more information\. |
| `continue_training_existing_model` | *flag*                     |                                                                                                                                                                                                         |
| `structure_type`                   | `TAN``MarkovBlanket`       | Select the structure to be used when building the Bayesian network\.                                                                                                                                    |
| `use_feature_selection`            | *flag*                     |                                                                                                                                                                                                         |
| `parameter_learning_method`        | `Likelihood``Bayes`        | Specifies the method used to estimate the conditional probability tables between nodes where the values of the parents are known\.                                                                      |
| `mode`                             | `Expert``Simple`           |                                                                                                                                                                                                         |
| `missing_values`                   | *flag*                     |                                                                                                                                                                                                         |
| `all_probabilities`                | *flag*                     |                                                                                                                                                                                                         |
| `independence`                     | `Likelihood``Pearson`      | Specifies the method used to determine whether paired observations on two variables are independent of each other\.                                                                                     |
| `significance_level`               | *number*                   | Specifies the cutoff value for determining independence\.                                                                                                                                               |
| `maximal_conditioning_set`         | *number*                   | Sets the maximal number of conditioning variables to be used for independence testing\.                                                                                                                 |
| `inputs_always_selected`           | *\[field1 \.\.\. fieldN\]* | Specifies which fields from the dataset are always to be used when building the Bayesian network\.<br><br>Note: The target field is always selected\.                                                   |
| `maximum_number_inputs`            | *number*                   | Specifies the maximum number of input fields to be used in building the Bayesian network\.                                                                                                              |
| `calculate_variable_importance`    | *flag*                     |                                                                                                                                                                                                         |
| `calculate_raw_propensities`       | *flag*                     |                                                                                                                                                                                                         |
| `calculate_adjusted_propensities`  | *flag*                     |                                                                                                                                                                                                         |
| `adjusted_propensity_partition`    | `Test``Validation`         |                                                                                                                                                                                                         |

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