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

Auto Numeric node iconThe Auto Numeric node estimates and compares models for continuous numeric range outcomes using a number of different methods. The node works in the same manner as the Auto Classifier node, allowing you to choose the algorithms to use and to experiment with multiple combinations of options in a single modeling pass. Supported algorithms include neural networks, C&R Tree, CHAID, linear regression, generalized linear regression, and support vector machines (SVM). Models can be compared based on correlation, relative error, or number of variables used.

Example

node = stream.create("autonumeric", "My node")
node.setPropertyValue("ranking_measure", "Correlation")
node.setPropertyValue("ranking_dataset", "Training")
node.setPropertyValue("enable_correlation_limit", True)
node.setPropertyValue("correlation_limit", 0.8)
node.setPropertyValue("calculate_variable_importance", True)
node.setPropertyValue("neuralnetwork", True)
node.setPropertyValue("chaid", False)
Table 1. autonumericnode properties
autonumericnode Properties Values Property description
custom_fields flag If True, custom field settings will be used instead of type node settings.
target field The Auto Numeric node requires a single target and one or more input fields. Weight and frequency fields can also be specified. See Common modeling node properties for more information.
inputs [field1 … field2]  
partition field  
use_frequency flag  
frequency_field field  
use_weight flag  
weight_field field  
use_partitioned_data flag If a partition field is defined, only the training data is used for model building.
ranking_measure Correlation NumberOfFields  
ranking_dataset Test Training  
number_of_models integer Number of models to include in the model nugget. Specify an integer between 1 and 100.
calculate_variable_importance flag  
enable_correlation_limit flag  
correlation_limit integer  
enable_number_of_fields_limit flag  
number_of_fields_limit integer  
enable_relative_error_limit flag  
relative_error_limit integer  
enable_model_build_time_limit flag  
model_build_time_limit integer  
enable_stop_after_time_limit flag  
stop_after_time_limit integer  
stop_if_valid_model flag  
<algorithm> flag Enables or disables the use of a specific algorithm.
<algorithm>.<property> string Sets a property value for a specific algorithm. See Setting algorithm properties for more information.
use_cross_validation boolean Instead of using a single partition, a cross validation partition is used.
number_of_folds integer N fold parameter for cross validation, with range from 3 to 10.
set_random_seed boolean Setting a random seed allows you to replicate analyses. Specify an integer or click Generate, which will create a pseudo-random integer between 1 and 2147483647, inclusive. By default, analyses are replicated with seed 229176228.
random_seed integer Random seed
filter_individual_model_output boolean Removes from the output all of the additional fields generated by the individual models that feed into the Ensemble node. Select this option if you're interested only in the combined score from all of the input models. Ensure that this option is deselected if, for example, you want to use an Analysis node or Evaluation node to compare the accuracy of the combined score with that of each of the individual input models.
calculate_standard_error boolean For a continuous (numeric range) target, a standard error calculation runs by default to calculate the difference between the measured or estimated values and the true values; and to show how close those estimates matched.