linearnode properties

Last updated: Jan 18, 2024

Linear node icon 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")

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.