Predictive Systems Calibrator (Regression)

Creates a calibration table with ranking for further calibration of the test data sets. Ranking is a sorting by absolute error (Alpha) provided by predictor nodes in descending order. In order to use normalization Prediction Variance or estimate of difficulty - Sigma (needs to be calculated separately) should be provided. Alpha values are used for further cumulative distribution function (CDF) calculation. This node is supposed to be used WITH Conformal Calibration and Conformal Prediction loops.

Options

Target column
A column with target column that is being predicted.
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.
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.

Input Ports

Icon
Table provided by predictor nodes fitted predictions.

Output Ports

Icon
Calibration table with ranks for each sample.

Popular Predecessors

  • No recommendations found

Views

This node has no views

Workflows

Links

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.