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

Neural Net node iconThe Neural Net node uses a simplified model of the way the human brain processes information. It works by simulating a large number of interconnected simple processing units that resemble abstract versions of neurons. Neural networks are powerful general function estimators and require minimal statistical or mathematical knowledge to train or apply.

Example

node = stream.create("neuralnetwork", "My node")
# Build Options tab - Objectives panel
node.setPropertyValue("objective", "Standard")
# Build Options tab - Ensembles panel
node.setPropertyValue("combining_rule_categorical", "HighestMeanProbability")
Table 1. neuralnetworknode properties
neuralnetworknode Properties Values Property description
targets [field1 ... fieldN] Specifies target fields.
inputs [field1 ... fieldN] Predictor fields used by the model.
splits [field1 ... fieldN Specifies the field or fields to use for split modeling.
use_partition flag If a partition field is defined, this option ensures that only data from the training partition is used to build the model.
continue flag Continue training existing model.
objective
Standard
Bagging
Boosting
psm
 psm is used for very large datasets, and requires a server connection.
method
MultilayerPerceptron
RadialBasisFunction
  
use_custom_layers flag  
first_layer_units number  
second_layer_units number  
use_max_time flag  
max_time number  
use_max_cycles flag  
max_cycles number  
use_min_accuracy flag  
min_accuracy number  
combining_rule_categorical
Voting
HighestProbability
HighestMeanProbability
 
combining_rule_continuous
MeanMedian
  
component_models_n number  
overfit_prevention_pct number  
use_random_seed flag  
random_seed number  
missing_values
listwiseDeletion
missingValueImputation
  
use_model_name boolean  
model_name string  
confidence
onProbability
onIncrease
  
score_category_probabilities flag  
max_categories number  
score_propensity flag  
use_custom_name flag  
custom_name string  
tooltip string  
keywords string  
annotation string  
calculate_variable_importance boolean For models that produce an appropriate measure of importance, you can display a chart that indicates the relative importance of each predictor in estimating the model. Typically, you'll want to focus your modeling efforts on the predictors that matter most, and consider dropping or ignoring those that matter least.