bayesnetnode properties
With the Bayesian Network (Bayes Net) node, you can build a probability model by combining observed and recorded evidence with realworld 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.
Example
node = stream.create("bayesnet", "My node")
node.setPropertyValue("continue_training_existing_model", True)
node.setPropertyValue("structure_type", "MarkovBlanket")
node.setPropertyValue("use_feature_selection", True)
# Expert tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("all_probabilities", True)
node.setPropertyValue("independence", "Pearson")
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 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.
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
