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Last updated: Feb 11, 2025
The 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")
Properties |
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[field1 ... fieldN] | Specifies target fields. |
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[field1 ... fieldN] | Predictor fields used by the model. |
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[field1 ... fieldN | Specifies the field or fields to use for split modeling. |
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flag | If a partition field is defined, this option ensures that only data from the training partition is used to build the model. |
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flag | Continue training existing model. |
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is used for very large datasets, and requires a server
connection. |
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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. |
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