Last updated: Jan 17, 2024
Linear regression is a common statistical technique for
summarizing data and making predictions by fitting a straight line or surface that minimizes the
discrepancies between predicted and actual output values.
Example
node = stream.create("regression", "My node")
# "Fields" tab
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("target", "Age")
node.setPropertyValue("inputs", ["Na", "K"])
node.setPropertyValue("partition", "Test")
node.setPropertyValue("use_weight", True)
node.setPropertyValue("weight_field", "Drug")
# "Model" tab
node.setPropertyValue("use_model_name", True)
node.setPropertyValue("model_name", "Regression Age")
node.setPropertyValue("use_partitioned_data", True)
node.setPropertyValue("method", "Stepwise")
node.setPropertyValue("include_constant", False)
# "Expert" tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("complete_records", False)
node.setPropertyValue("tolerance", "1.0E-3")
# "Stepping..." section
node.setPropertyValue("stepping_method", "Probability")
node.setPropertyValue("probability_entry", 0.77)
node.setPropertyValue("probability_removal", 0.88)
node.setPropertyValue("F_value_entry", 7.0)
node.setPropertyValue("F_value_removal", 8.0)
# "Output..." section
node.setPropertyValue("model_fit", True)
node.setPropertyValue("r_squared_change", True)
node.setPropertyValue("selection_criteria", True)
node.setPropertyValue("descriptives", True)
node.setPropertyValue("p_correlations", True)
node.setPropertyValue("collinearity_diagnostics", True)
node.setPropertyValue("confidence_interval", True)
node.setPropertyValue("covariance_matrix", True)
node.setPropertyValue("durbin_watson", True)
regressionnode Properties |
Values | Property description |
|---|---|---|
target
|
field | Regression models require a single target field and one or more input fields. A weight field can also be specified. See the topic Common modeling node properties for more information. |
method
|
Enter Stepwise Backwards Forwards |
|
include_constant
|
flag | |
use_weight
|
flag | |
weight_field
|
field | |
mode
|
Simple Expert |
|
complete_records
|
flag | |
tolerance
|
1.0E-11.0E-2 1.0E-3 1.0E-4 1.0E-5 1.0E-6 1.0E-7 1.0E-8 1.0E-9 1.0E-10 1.0E-11 1.0E-12 |
Use double quotes for arguments. |
stepping_method
|
useP useF |
useP : use probability of F
useF: use F value |
probability_entry
|
number | |
probability_removal
|
number | |
F_value_entry
|
number | |
F_value_removal
|
number | |
selection_criteria
|
flag | |
confidence_interval
|
flag | |
covariance_matrix
|
flag | |
collinearity_diagnostics
|
flag | |
regression_coefficients
|
flag | |
exclude_fields
|
flag | |
durbin_watson
|
flag | |
model_fit
|
flag | |
r_squared_change
|
flag | |
p_correlations
|
flag | |
descriptives
|
flag | |
calculate_variable_importance
|
flag | |
residuals |
boolean | Statistics for the residuals (or the differences between predicted values and actual values). |