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discriminantnode properties
discriminantnode properties
Last updated: Feb 12, 2025
discriminantnode properties

Discriminant node iconDiscriminant analysis makes more stringent assumptions than logistic regression, but can be a valuable alternative or supplement to a logistic regression analysis when those assumptions are met.

Example

node = stream.create("discriminant", "My node")
node.setPropertyValue("target", "custcat")
node.setPropertyValue("use_partitioned_data", False)
node.setPropertyValue("method", "Stepwise")
Table 1. discriminantnode properties
discriminantnode Properties Values Property description
target field Discriminant models require a single target field and one or more input fields. Weight and frequency fields aren't used. See Common modeling node properties for more information.
method Enter
Stepwise 		 
mode Simple
Expert 		 
prior_probabilities AllEqual
ComputeFromSizes 		 
covariance_matrix WithinGroups
SeparateGroups 		 
means flag Statistics options in the node properties under Expert Options.
univariate_anovas flag  
box_m flag  
within_group_covariance flag  
within_groups_correlation flag  
separate_groups_covariance flag  
total_covariance flag  
fishers flag  
unstandardized flag  
casewise_results flag Classification options in the node properties under Expert Options.
limit_to_first number Default value is 10.
summary_table flag  
leave_one_classification flag  
separate_groups_covariance flag Matrices option Separate-groups covariance.
territorial_map flag  
combined_groups flag Plot option Combined-groups.
separate_groups flag Plot option Separate-groups.
summary_of_steps flag  
F_pairwise flag  
stepwise_method WilksLambda
UnexplainedVariance
MahalanobisDistance
SmallestF
RaosV 		 
V_to_enter number  
criteria UseValue
UseProbability 		 
F_value_entry number Default value is 3.84.
F_value_removal number Default value is 2.71.
probability_entry number Default value is 0.05.
probability_removal number Default value is 0.10.
calculate_variable_importance flag  
calculate_raw_propensities flag  
calculate_adjusted_propensities flag  
adjusted_propensity_partition Test
Validation