bayesnetnode properties
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.
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
|