Polynomial Regression Learner

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementPolynomial Regression Learner

This node performs polynomial regression on the input data and computes the coefficients that minimize the squared error. The user must choose one column as target (dependent variable) and a number of independent variables. By default, polynomials with degree 2 are computed, which can be changed in the dialog.
If the optional PMML inport is connected and contains preprocessing operations in the TransformationDictionary those are added to the learned model.


Regression settings

Target column
The column that contains the dependent "target" variable.
Polynomial degree
The maximum degree the polynomial regression function should have.
Column Selection
Select the columns containing the independent variables and move them to the "include" list.

View settings

Number of data points to show in view
This option can be use to change the number of data points in the node view if e.g. there are too many points. The default value is 10,000 points.

Input Ports

The input samples, which of the columns are used as independent variables can be configured in the dialog. The input must not contain missing values, you have to fix them by e.g. using the Missing Values node.
Optional PMML port object containing preprocessing operations.

Output Ports

Training data classified with the learned model and the corresponding errors.
The computed regression coefficients as a model for use in the Polynomial Regression Predictor.

Popular Predecessors

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Popular Successors

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Learned Coefficients
Shows all learned coefficients all attributes.
Scatter Plot
Shows the data points and the regression function in one dimension.


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