Polynomial 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.

Options

Target column (dependent variable)
The column that contains the dependent "target" variable.
Maximum polynomial degree
The maximum degree the polynomial regression function should have.
Independent variables
Select the columns containing the independent variables and move them to the "include" list.
Missing values in input data
Define how to handle rows with missing values in the input data.
  • Fail: Stops execution with an error if missing values occur in the input data.
  • Ignore: Skips rows containing missing values so the regression model is built only on complete rows.
Number of data points to show in view
This option can be used 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

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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.

Output Ports

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The computed regression coefficients as a PMML model for use in the Regression Predictor.
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Training data classified with the learned model and the corresponding errors.
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The computed regression coefficients as a table with statistics related to the training data.

Views

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

Workflows

Links

Developers

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