Last updated: Feb 11, 2025
The 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)
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
|