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.
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
CorrelationNumberOfFields
ranking_dataset
TestTraining
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.
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.
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