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

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.

Output Ports

: 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.

Popular Predecessors

Popular Successors

Views

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

Workflows

Developers

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.