Regression Predictor

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 ReplacementRegression Predictor

Predicts the response using a regression model. The node needs to be connected to a regression node model* and some test data. It is only executable if the test data contains the columns that are used by the learner model. This node appends a new columns to the input table containing the prediction for each row.

*You can use nodes like the linear regression, polynomial regression and the logistic regression node to create regression models.


Append columns with predicted probabilities
This has only an effect for target columns with nominal data. The number of appended columns is equal to the number of categories of the target column. They represent the probability that a row in the input data falls in a specific category.

Input Ports

The regression model
Table for prediction. Missing values will give missing values in the output.

Output Ports

Table from input with an additional prediction column.


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