c50node properties

C5.0 node iconThe C5.0 node builds either a decision tree or a rule set. The model works by splitting the sample based on the field that provides the maximum information gain at each level. The target field must be categorical. Multiple splits into more than two subgroups are allowed.


node = stream.create("c50", "My node")
# "Model" tab
node.setPropertyValue("use_model_name", False)
node.setPropertyValue("model_name", "C5_Drug")
node.setPropertyValue("use_partitioned_data", True)
node.setPropertyValue("output_type", "DecisionTree")
node.setPropertyValue("use_xval", True)
node.setPropertyValue("xval_num_folds", 3)
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("favor", "Generality")
node.setPropertyValue("min_child_records", 3)
# "Costs" tab
node.setPropertyValue("use_costs", True)
node.setPropertyValue("costs", [["drugA", "drugX", 2]])
Table 1. c50node properties
c50node Properties Values Property description
target field C50 models use a single target field and one or more input fields. You can also specify a weight field. See Common modeling node properties for more information.
output_type DecisionTree RuleSet  
group_symbolics flag  
use_boost flag  
boost_num_trials number  
use_xval flag  
xval_num_folds number  
mode Simple Expert  
favor Accuracy Generality Favor accuracy or generality.
expected_noise number  
min_child_records number  
pruning_severity number  
use_costs flag  
costs structured This is a structured property. See the example for usage.
use_winnowing flag  
use_global_pruning flag On (True) by default.
calculate_variable_importance flag  
calculate_raw_propensities flag  
calculate_adjusted_propensities flag  
adjusted_propensity_partition Test Validation