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Last updated: Feb 11, 2025
Linear regression models predict a continuous target based on linear relationships
between the target and one or more predictors.
Example
node = stream.create("linear", "My node")
# Build Options tab - Objectives panel
node.setPropertyValue("objective", "Standard")
# Build Options tab - Model Selection panel
node.setPropertyValue("model_selection", "BestSubsets")
node.setPropertyValue("criteria_best_subsets", "ASE")
# Build Options tab - Ensembles panel
node.setPropertyValue("combining_rule_categorical", "HighestMeanProbability")
Properties |
Values | Property description |
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field | Specifies a single target field. |
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[field1 ... fieldN] | Predictor fields used by the model. |
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flag | |
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is used for very large datasets, and requires a server
connection. |
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string | |
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flag | |
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string | |
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string | |
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string | |
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boolean | Perform model effect tests for each regression effect. |
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double | This is the interval of confidence used to compute estimates of the model coefficients. Specify a value greater than 0 and less than 100. The default is 95. |
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double | If F Statistics is chosen as the criterion, then at each step the effect that has the smallest p-value less than the specified threshold is added to the model (include effects with p-values less than). The default is 0.05. |
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double | Any effects in the model with a p-value greater than the specified threshold are removed (remove effects with p-values greater than). The default is 0.10. |
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