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03_​Counterfactual_​Explanations_​for_​scikit-learn_​(Python)

Counterfactual Explanations for scikit-learn (Python)

This application is a simple example of using Conterfactual Explanations (Python) component to identify the Counterfactual Instances for a Binary classification model trained with Scikit-learn in Python.

The Python object readers load the pickled model and the pickled Python object which is used for nomalisation of features. The component (in blue) can be used to select the instances to be used for Counterfactual Explanations.

Counterfactual Explanations for Scikit-learn model trained in Python input 0: trained model (Keras or Scikit-Learn)input 1 : preprocessing pickled fileinput 2 : instances to explain---ouput : CounterfactualsLoad model Load preprocessSampleAdult Datasettop 10 rowsCounterfactualExplanations (Python) Conda EnvironmentPropagation Python ObjectReader Python ObjectReader Table Reader Row Filter Counterfactual Explanations for Scikit-learn model trained in Python input 0: trained model (Keras or Scikit-Learn)input 1 : preprocessing pickled fileinput 2 : instances to explain---ouput : CounterfactualsLoad model Load preprocessSampleAdult Datasettop 10 rowsCounterfactualExplanations (Python) Conda EnvironmentPropagation Python ObjectReader Python ObjectReader Table Reader Row Filter

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: mpatt
Created at: 2021-04-08
On NodePit since: 2021-04-21
Last update: 2026-07-19
Created with KNIME version: v4.3.2
Tags: Interpretable MLPython and KNIMECounterfactual InstancesCounterfactual ExplainationsBinary ClassificationExplainable AIXAIInterpretable Machine Learning

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