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The 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.
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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 the topic 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 are 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
|
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stop_after_time_limit
|
integer
|
|
stop_if_valid_model
|
flag
|
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<algorithm>
|
flag
|
Enables or disables the use of a specific algorithm. |
<algorithm>.<property>
|
string
|
Sets a property value for a specific algorithm. See the topic Setting Algorithm Properties
for more information. |
