Last updated: Jan 17, 2024
The Self-Learning Response Model (SLRM) node enables you to build a model in which
a single new case, or small number of new cases, can be used to reestimate the model without having
to retrain the model using all data.
Example
node = stream.create("slrm", "My node")
node.setPropertyValue("target", "Offer")
node.setPropertyValue("target_response", "Response")
node.setPropertyValue("inputs", ["Cust_ID", "Age", "Ave_Bal"])
slrmnode Properties |
Values | Property description |
|---|---|---|
target
|
field | The target field must be a nominal or flag field. A frequency field can also be specified. See Common modeling node properties for more information. |
target_response
|
field | Type must be flag. |
continue_training_existing_model
|
flag | |
target_field_values
|
flag | Use all: Use all values from source. Specify: Select values required. |
target_field_values_specify
|
[field1 ... fieldN] | |
include_model_assessment
|
flag | |
model_assessment_random_seed
|
number | Must be a real number. |
model_assessment_sample_size
|
number | Must be a real number. |
model_assessment_iterations
|
number | Number of iterations. |
display_model_evaluation
|
flag | |
max_predictions
|
number | |
randomization
|
number | |
scoring_random_seed
|
number | |
sort
|
Ascending
Descending
|
Specifies whether the offers with the highest or lowest scores will be displayed first. |
model_reliability
|
flag | |
calculate_variable_importance
|
flag |