The following node is available in the Open Source KNIME predictive analytics and data mining platform version 2.7.1. Discover over 1000 other nodes, as well as enterprise functionality at http://knime.com.
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.
0 | 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. |
1 | Optional PMML port object containing preprocessing operations. |
0 | Training data classified with the learned model and the corresponding errors. |
1 | The computed regression coefficients as a model for use in the Polynomial Regression Predictor. |