About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Last updated: Feb 11, 2025
The Ensemble node combines two or more model nuggets to obtain more accurate
predictions than can be gained from any one model.
Example
# Create and configure an Ensemble node
node = stream.create("ensemble", "My node")
node.setPropertyValue("ensemble_target_field", "response")
node.setPropertyValue("filter_individual_model_output", False)
node.setPropertyValue("flag_ensemble_method", "ConfidenceWeightedVoting")
node.setPropertyValue("flag_voting_tie_selection", "HighestConfidence")
properties |
Data type | Property description |
---|---|---|
|
field | Specifies the target field for all models used in the ensemble. |
|
flag | Specifies whether scoring results from individual models should be suppressed. |
|
|
Specifies the method used to determine the ensemble score. This setting applies only if the selected target is a flag field. |
|
|
Specifies the method used to determine the ensemble score. This setting applies only if the selected target is a nominal field. |
|
|
If a voting method is selected, specifies how ties are resolved. This setting applies only if the selected target is a flag field. |
|
|
If a voting method is selected, specifies how ties are resolved. This setting applies only if the selected target is a nominal field. |
|
flag | If the target field is continuous, a standard error calculation is run by default to calculate the difference between the measured or estimated values and the true values; and to show how close those estimates matched. |
Was the topic helpful?
0/1000