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01_​Compute_​LIMEs

Workflow

Compute Local Model-agnostic Explanations (LIMEs)
LIMEmachine learning interpretabilitymliexplanationinstance-level explanationreason codeexplaininterpretmodelmachine learningXGBXGBoost
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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Nodes

01_​Compute_​LIMEs consists of the following 107 nodes(s):

Plugins

01_​Compute_​LIMEs contains nodes provided by the following 12 plugin(s):