Predictive Systems Predictor (Regression)

The node returns cumulative distribution function (CDF) for each sample as List column. CDF for the sample is calculated with predictions and conformal regression calibration table. It is optionally available to to predictions normalization with difficulty measure - Sigma (needs to be calculated separately). The purpose of this node is similar to Conformal Predictor or Conformal Predictor and Classifier (Regression) nodes. This node is supposed to be used WITH Conformal Calibration and Conformal Prediction loops.

Options

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 predictive systems (advanced)KNIME Hub

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