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03_​XAI_​Credit_​Scoring_​Example_​Global_​Feature_​Importance

Explaining Global Feature Importance of a Credit Scoring Model
This workflow demonstrates the usage of the verified Global Feature Importance component developed to interpret globalbehavior 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" classPartitioning Global FeatureImportance AutoML CSV Reader Equal Size Sampling This workflow demonstrates the usage of the verified Global Feature Importance component developed to interpret globalbehavior 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" classPartitioning Global FeatureImportance AutoML CSV Reader Equal Size Sampling

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Created by: paolotamag
Created at: 2020-06-24
On NodePit since: 2021-08-31
Last update: 2024-04-18
Created with KNIME version: v4.4.0
Tags: autoMLintegrated deploymentXAIMLImachine learning interpretabilityexplainable AIcredit scoringglobal surrogate modelpermutation feature importance

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