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01_​Compute_​LIMEs

Compute Local Model-agnostic Explanations (LIMEs)
Local Interpretable Model-agnostic Explanation (LIME)This is an example of a computing explanation using LIME.An XGBoost model was picked, but any model and its set of Learner and Predictor nodes can be used.See View -> Description for more information about what the workflow does. collectsexplanationsred wine data top: 90% train setbottom: 10% test settrain the modelscoresamplestop input : test set instance rows to be explainedbottom input : test set distributionfew wines with high sulphates +few wines with low sulphatestrains a local GLMfor each input instanceto generate a Local Inter. Model-agn. ExplanationLoop End File Reader Partitioning XGBoost TreeEnsemble Learner XGBoost Predictor LIME Loop Start SelectInstance Rows VisualizeExplanations Data Preparation ExplanationsPost-processing Compute LIME Local Interpretable Model-agnostic Explanation (LIME)This is an example of a computing explanation using LIME.An XGBoost model was picked, but any model and its set of Learner and Predictor nodes can be used.See View -> Description for more information about what the workflow does. collectsexplanationsred wine datatop: 90% train setbottom: 10% test settrain the modelscoresamplestop input : test set instance rows to be explainedbottom input : test set distributionfew wines with high sulphates +few wines with low sulphatestrains a local GLMfor each input instanceto generate a Local Inter. Model-agn. ExplanationLoop End File Reader Partitioning XGBoost TreeEnsemble Learner XGBoost Predictor LIME Loop Start SelectInstance Rows VisualizeExplanations Data Preparation ExplanationsPost-processing Compute LIME

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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: Adrian Nembach
Created at: 2019-06-21
On NodePit since: 2020-07-06
Last update: 2024-12-17
Created with KNIME version: v4.1.3
Tags: LIMEmachine learning interpretabilitymliexplanationinstance-level explanationreason codeexplaininterpretmodelmachine learningXGBXGBoost

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