Seemingly Unrelated Regression
(AI Studio Core)
Synopsis
This operator performs regressions on coherent ExampleSets with different labels and will take into account the correlation of residuals to build the model. It creates a composite regression model optimized for predicting all label attributes at the same time.Description
This operator creates a model that consists of multiple regressions targeting different label attributes for the same set of examples. Once each single linear regression is built for each input ExampleSet (delivered at unrelated example sets ports), the covariance of the residuals is used to improve the quality of predictions that are influenced by effects not captured by the attributes.
The first port is dedicated for the original ExampleSet, which must contain the union of all attributes delivered at unrelated example sets input ports. Moreover, all input ExampleSets must have the same number of examples.
Practically, all seemingly unrelated input ExampleSets should have the same examples as the original one, but only contain a subset of its attributes and its own dedicated label.
Please note that whenever a Seemingly Unrelated Regression model is applied, the prediction attributes will not have prediction roles, as only one attribute can have this role at a time. You can add prediction role to a single attribute by using Set Role operator.
Input
training set (IOObject)- unrelated example sets (IOObject)
Output
model (Model)- example set (Data Table)
This is an example set output port
Parameters
- feature selectionThe feature selection method used during regression.
- alphaThis is the significance level of the t-test.
- max iterationsThe maximal number of rounds for iterative selection.
- forward alphaThis is the alpha level for the used t-test for selecting attributes.
- backward alphaThis is the alpha level for the used t-test for deselecting attributes.
- eliminate colinear featuresIndicates if the algorithm should try to delete colinear features during the regression.
- min toleranceThe minimum tolerance for the removal of colinear features.
- use biasIndicates if an intercept value should be calculated.
- ridgeThe ridge parameter used for ridge regression. A value of zero switches to ordinary least squares estimate.