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linearnode properties

linearnode properties

Last updated: Feb 11, 2025
linearnode properties

Linear node iconLinear regression models predict a continuous target based on linear relationships between the target and one or more predictors.

Example

node = stream.create("linear", "My node")
# Build Options tab - Objectives panel
node.setPropertyValue("objective", "Standard")
# Build Options tab - Model Selection panel
node.setPropertyValue("model_selection", "BestSubsets")
node.setPropertyValue("criteria_best_subsets", "ASE")
# Build Options tab - Ensembles panel
node.setPropertyValue("combining_rule_categorical", "HighestMeanProbability")
Table 1. linearnode properties
linearnode Properties Values Property description
target field Specifies a single target field.
inputs [field1 ... fieldN] Predictor fields used by the model.
continue_training_existing_model flag  
objective
Standard
Bagging
Boosting
psm
 psm is used for very large datasets, and requires a server connection.
use_auto_data_preparation flag  
confidence_level number  
model_selection
ForwardStepwise
BestSubsets
None
  
criteria_forward_stepwise
AICC
Fstatistics
AdjustedRSquare
ASE
  
probability_entry number  
probability_removal number  
use_max_effects flag  
max_effects number  
use_max_steps flag  
max_steps number  
criteria_best_subsets
AICC
AdjustedRSquare
ASE
  
combining_rule_continuous
Mean
Median
  
component_models_n number  
use_random_seed flag  
random_seed number  
use_custom_model_name flag  
custom_model_name string  
use_custom_name flag  
custom_name string  
tooltip string  
keywords string  
annotation string  
perform_model_effect_tests boolean Perform model effect tests for each regression effect.
confidence_level double This is the interval of confidence used to compute estimates of the model coefficients. Specify a value greater than 0 and less than 100. The default is 95.
probability_entry double If F Statistics is chosen as the criterion, then at each step the effect that has the smallest p-value less than the specified threshold is added to the model (include effects with p-values less than). The default is 0.05.
probability_removal double Any effects in the model with a p-value greater than the specified threshold are removed (remove effects with p-values greater than). The default is 0.10.