Last updated: Feb 11, 2025
The 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.
Example
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]])
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
|