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