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Deprecated**KNIME Base Nodes** version **4.1.0.v201912041211** by **KNIME AG, Zurich, Switzerland**

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.

- 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.

- 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.

- 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.

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

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

To use this node in KNIME, install KNIME Core from the following update site:

KNIME 4.1

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