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04_​XAI_​Credit_​Scoring_​Example_​Local_​Explanation_​View

Finding Counterfactual Explanations and Local Feature Importance for a Selected Prediction of a Credit Scoring Model
This workflow demonstrates the usage of the verified Local Explanation View component developed to provide counterfactual explanations for a single prediction andinterpret local behavior of a black box machine learning model on the example of the credit scoring. Top: train setBottom: test setStandard pre-processing & black-box model trainingNeural NetworkCredit scoring dataUndersamplingof the frequent"creditworthy" classSelect applicant wrongly rejected by the model (false negative)Partitioning Predictor AutoML Local ExplanationView Row Sampling CSV Reader Equal Size Sampling Scatter PlotSelection This workflow demonstrates the usage of the verified Local Explanation View component developed to provide counterfactual explanations for a single prediction andinterpret local behavior of a black box machine learning model on the example of the credit scoring. Top: train setBottom: test setStandard pre-processing & black-box model trainingNeural NetworkCredit scoring dataUndersamplingof the frequent"creditworthy" classSelect applicant wrongly rejected by the model (false negative)Partitioning Predictor AutoML Local ExplanationView Row Sampling CSV Reader Equal Size Sampling Scatter PlotSelection

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Created by: paolotamag
Created at: 2020-06-24
On NodePit since: 2021-08-31
Last update: 2024-04-27
Created with KNIME version: v4.4.0
Tags: autoMLintegrated deploymentXAIMLImachine learning interpretabilityexplainable AIcredit scoringcounterfactual explanationscounterfactualslocal surrogate modellocal feature importance

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