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cartnode properties
cartnode properties
Last updated: Feb 12, 2025
cartnode properties

C&R Tree node iconThe Classification and Regression (C&R) Tree node generates a decision tree that allows you to predict or classify future observations. The method uses recursive partitioning to split the training records into segments by minimizing the impurity at each step, where a node in the tree is considered "pure" if 100% of cases in the node fall into a specific category of the target field. Target and input fields can be numeric ranges or categorical (nominal, ordinal, or flags); all splits are binary (only two subgroups).

Example

node = stream.createAt("cart", "My node", 200, 100)
# "Fields" tab
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("target", "Drug")
node.setPropertyValue("inputs", ["Age", "BP", "Cholesterol"])
# "Build Options" tab, "Objective" panel
node.setPropertyValue("model_output_type", "InteractiveBuilder")
node.setPropertyValue("use_tree_directives", True)
node.setPropertyValue("tree_directives", """Grow Node Index 0 Children 1 2
Grow Node Index 2 Children 3 4""")
# "Build Options" tab, "Basics" panel
node.setPropertyValue("prune_tree", False)
node.setPropertyValue("use_std_err_rule", True)
node.setPropertyValue("std_err_multiplier", 3.0)
node.setPropertyValue("max_surrogates", 7)
# "Build Options" tab, "Stopping Rules" panel
node.setPropertyValue("use_percentage", True)
node.setPropertyValue("min_parent_records_pc", 5)
node.setPropertyValue("min_child_records_pc", 3)
# "Build Options" tab, "Advanced" panel
node.setPropertyValue("min_impurity", 0.0003)
node.setPropertyValue("impurity_measure", "Twoing")
# "Model Options" tab
node.setPropertyValue("use_model_name", True)
node.setPropertyValue("model_name", "Cart_Drug")
Table 1. cartnode properties
cartnode Properties Values Property description
target field C&R Tree models require a single target and one or more input fields. A frequency field can also be specified. See the topic Common modeling node properties for more information.
continue_training_existing_model flag  
objective Standard Boosting Bagging psm psm is used for very large datasets, and requires a Server connection.
model_output_type Single InteractiveBuilder  
use_tree_directives flag  
tree_directives string Specify directives for growing the tree. Directives can be wrapped in triple quotes to avoid escaping newlines or quotes. Note that directives may be highly sensitive to minor changes in data or modeling options and may not generalize to other datasets.
use_max_depth Default Custom  
max_depth integer Maximum tree depth, from 0 to 1000. Used only if use_max_depth = Custom.
prune_tree flag Prune tree to avoid overfitting.
use_std_err flag Use maximum difference in risk (in Standard Errors).
std_err_multiplier number Maximum difference.
max_surrogates number Maximum surrogates.
use_percentage flag  
min_parent_records_pc number  
min_child_records_pc number  
min_parent_records_abs number  
min_child_records_abs number  
use_costs flag  
costs structured Structured property.
priors Data Equal Custom  
custom_priors structured Structured property.
adjust_priors flag  
trails number Number of component models for boosting or bagging.
set_ensemble_method Voting HighestProbability HighestMeanProbability Default combining rule for categorical targets.
range_ensemble_method Mean Median Default combining rule for continuous targets.
large_boost flag Apply boosting to very large data sets.
min_impurity number  
impurity_measure Gini Twoing Ordered  
train_pct number Overfit prevention set.
set_random_seed flag Replicate results option.
seed number  
calculate_variable_importance flag  
calculate_raw_propensities flag  
calculate_adjusted_propensities flag  
adjusted_propensity_partition Test Validation