linearnode properties
Linear 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")
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 pvalue less than the specified threshold is added to the model (include effects with pvalues less than). The default is 0.05. 
probability_removal

double  Any effects in the model with a pvalue greater than the specified threshold are removed (remove effects with pvalues greater than). The default is 0.10. 