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

Quest node iconThe Quest node provides a binary classification method for building decision trees, designed to reduce the processing time required for large C&R Tree analyses while also reducing the tendency found in classification tree methods to favor inputs that allow more splits. Input fields can be numeric ranges (continuous), but the target field must be categorical. All splits are binary.

Example

node = stream.create("quest", "My node")
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("target", "Drug")
node.setPropertyValue("inputs", ["Age", "Na", "K", "Cholesterol", "BP"])
node.setPropertyValue("model_output_type", "InteractiveBuilder")
node.setPropertyValue("use_tree_directives", True)
node.setPropertyValue("max_surrogates", 5)
node.setPropertyValue("split_alpha", 0.03)
node.setPropertyValue("use_percentage", False)
node.setPropertyValue("min_parent_records_abs", 40)
node.setPropertyValue("min_child_records_abs", 30)
node.setPropertyValue("prune_tree", True)
node.setPropertyValue("use_std_err", True)
node.setPropertyValue("std_err_multiplier", 3)
Table 1. questnode properties
questnode Properties Values Property description
target field Quest models require a single target and one or more input fields. A frequency field can also be specified. See 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  
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.
split_alpha number Significance level for splitting.
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