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logregnode properties

logregnode properties

Logistic node iconLogistic regression is a statistical technique for classifying records based on values of input fields. It is analogous to linear regression but takes a categorical target field instead of a numeric range.

Multinomial Example

node = stream.create("logreg", "My node")
# "Fields" tab
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("target", "Drug")
node.setPropertyValue("inputs", ["BP", "Cholesterol", "Age"])
node.setPropertyValue("partition", "Test")
# "Model" tab
node.setPropertyValue("use_model_name", True)
node.setPropertyValue("model_name", "Log_reg Drug")
node.setPropertyValue("use_partitioned_data", True)
node.setPropertyValue("method", "Stepwise")
node.setPropertyValue("logistic_procedure", "Multinomial")
node.setPropertyValue("multinomial_base_category", "BP")
node.setPropertyValue("model_type", "FullFactorial")
node.setPropertyValue("custom_terms", [["BP", "Sex"], ["Age"], ["Na", "K"]])
node.setPropertyValue("include_constant", False)
# "Expert" tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("scale", "Pearson")
node.setPropertyValue("scale_value", 3.0)
node.setPropertyValue("all_probabilities", True)
node.setPropertyValue("tolerance", "1.0E-7")
# "Convergence..." section
node.setPropertyValue("max_iterations", 50)
node.setPropertyValue("max_steps", 3)
node.setPropertyValue("l_converge", "1.0E-3")
node.setPropertyValue("p_converge", "1.0E-7")
node.setPropertyValue("delta", 0.03)
# "Output..." section
node.setPropertyValue("summary", True)
node.setPropertyValue("likelihood_ratio", True)
node.setPropertyValue("asymptotic_correlation", True)
node.setPropertyValue("goodness_fit", True)
node.setPropertyValue("iteration_history", True)
node.setPropertyValue("history_steps", 3)
node.setPropertyValue("parameters", True)
node.setPropertyValue("confidence_interval", 90)
node.setPropertyValue("asymptotic_covariance", True)
node.setPropertyValue("classification_table", True)
# "Stepping" options
node.setPropertyValue("min_terms", 7)
node.setPropertyValue("use_max_terms", True)
node.setPropertyValue("max_terms", 10)
node.setPropertyValue("probability_entry", 3)
node.setPropertyValue("probability_removal", 5)
node.setPropertyValue("requirements", "Containment")

Binomial Example

node = stream.create("logreg", "My node")
# "Fields" tab
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("target", "Cholesterol")
node.setPropertyValue("inputs", ["BP", "Drug", "Age"])
node.setPropertyValue("partition", "Test")
# "Model" tab
node.setPropertyValue("use_model_name", False)
node.setPropertyValue("model_name", "Log_reg Cholesterol")
node.setPropertyValue("multinomial_base_category", "BP")
node.setPropertyValue("use_partitioned_data", True)
node.setPropertyValue("binomial_method", "Forwards")
node.setPropertyValue("logistic_procedure", "Binomial")
node.setPropertyValue("binomial_categorical_input", "Sex")
node.setKeyedPropertyValue("binomial_input_contrast", "Sex", "Simple")
node.setKeyedPropertyValue("binomial_input_category", "Sex", "Last")
node.setPropertyValue("include_constant", False)
# "Expert" tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("scale", "Pearson")
node.setPropertyValue("scale_value", 3.0)
node.setPropertyValue("all_probabilities", True)
node.setPropertyValue("tolerance", "1.0E-7")
# "Convergence..." section
node.setPropertyValue("max_iterations", 50)
node.setPropertyValue("l_converge", "1.0E-3")
node.setPropertyValue("p_converge", "1.0E-7")
# "Output..." section
node.setPropertyValue("binomial_output_display", "at_each_step")
node.setPropertyValue("binomial_goodness_of_fit", True)
node.setPropertyValue("binomial_iteration_history", True)
node.setPropertyValue("binomial_parameters", True)
node.setPropertyValue("binomial_ci_enable", True)
node.setPropertyValue("binomial_ci", 85)
# "Stepping" options
node.setPropertyValue("binomial_removal_criterion", "LR")
node.setPropertyValue("binomial_probability_removal", 0.2)
Table 1. logregnode properties
logregnode Properties Values Property description
target field Logistic regression models require a single target field and one or more input fields. Frequency and weight fields are not used. See Common modeling node properties for more information.
logistic_procedure Binomial Multinomial  
include_constant flag  
mode Simple Expert  
method Enter Stepwise Forwards Backwards BackwardsStepwise  
binomial_method Enter Forwards Backwards  
model_type MainEffects FullFactorial Custom When FullFactorial is specified as the model type, stepping methods will not run, even if specified. Instead, Enter will be the method used. If the model type is set to Custom but no custom fields are specified, a main-effects model will be built.
custom_terms [[BP Sex][BP][Age]]  
multinomial_base_category string Specifies how the reference category is determined.
binomial_categorical_input string  
binomial_input_contrast Indicator Simple Difference Helmert Repeated Polynomial Deviation Keyed property for categorical input that specifies how the contrast is determined. See the example for usage.
binomial_input_category First Last Keyed property for categorical input that specifies how the reference category is determined. See the example for usage.
scale None UserDefined Pearson Deviance  
scale_value number  
all_probabilities flag  
tolerance 1.0E-5 1.0E-6 1.0E-7 1.0E-8 1.0E-9 1.0E-10  
min_terms number  
use_max_terms flag  
max_terms number  
entry_criterion Score LR  
removal_criterion LR Wald  
probability_entry number  
probability_removal number  
binomial_probability_entry number  
binomial_probability_removal number  
requirements HierarchyDiscrete HierarchyAll Containment None  
max_iterations number  
max_steps number  
p_converge 1.0E-4 1.0E-5 1.0E-6 1.0E-7 1.0E-8 0  
l_converge 1.0E-1 1.0E-2 1.0E-3 1.0E-4 1.0E-5 0  
delta number  
iteration_history flag  
history_steps number  
summary flag  
likelihood_ratio flag  
asymptotic_correlation flag  
goodness_fit flag  
parameters flag  
confidence_interval number  
asymptotic_covariance flag  
classification_table flag  
stepwise_summary flag  
info_criteria flag  
monotonicity_measures flag  
binomial_output_display at_each_step at_last_step  
binomial_goodness_of_fit flag  
binomial_parameters flag  
binomial_iteration_history flag  
binomial_classification_plots flag  
binomial_ci_enable flag  
binomial_ci number  
binomial_residual outliers all  
binomial_residual_enable flag  
binomial_outlier_threshold number  
binomial_classification_cutoff number  
binomial_removal_criterion LR Wald Conditional  
calculate_variable_importance flag  
calculate_raw_propensities flag  
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