Conformal Regression

This nodes combines the functions of Conformal Calibrator (Regression) and Conformal Predictor and Classifier (Regression) nodes. The purpose of it is to make it simple for users to apply conformal regression. The node returns the prediction of conformal regression model based on the regression model and conformal regression calibration table. This node is supposed to be used WITHOUT Conformal Calibration and Conformal Prediction loops.

Options

Target column (calibration table)

A column with target column that is being predicted.

Prediction column

A column with predicted values.

Use normalization

In order to increase the informativeness, and to potentially minimize prediction regions, it is possible to obtain individual bounds for each sample, which is achieved using a normalized nonconformity function. In order to use normalization Prediction Variance or estimate of difficulty - Sigma (needs to be calculated separately) should be provided.

Difficulty column

The column that contains the values defining the Sigma. By default it is advisable to use Prediction Variance values.

Beta

A sensitivity parameter determining the relative importance of the normalization term.

Error rate (significance level)

Defines the tolerable percentage of the prediction errors.

Keep All Columns

If checked all columns from input table will be also included into output table along with calibration columns.

Keep ID column

If checked selected ID column from input table will be included into output table.

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Workflows

• Conformal prediction regression (simple)KNIME Hub
• Conformal_regression_by_RedfieldKNIME Hub

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