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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. training a local GLMfor each input instance to generate a Local Inter. Model-agn. Explanationtop: 90% train setbottom: 10% test setscoresamplestop input : test set instance rows to be explainedbottom input : test set distributionfew wines with high sulphates +few wines with low sulphatesred wine datatrain the modelcollectsexplanationsCompute LIME Partitioning XGBoost Predictor LIME Loop Start SelectInstance Rows VisualizeExplanations Data Preparation ExplanationsPost-processing File Reader XGBoost TreeEnsemble Learner Loop End 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. training a local GLMfor each input instance to generate a Local Inter. Model-agn. Explanationtop: 90% train setbottom: 10% test setscoresamplestop input : test set instance rows to be explainedbottom input : test set distributionfew wines with high sulphates +few wines with low sulphatesred wine datatrain the modelcollectsexplanationsCompute LIME Partitioning XGBoost Predictor LIME Loop Start SelectInstance Rows VisualizeExplanations Data Preparation ExplanationsPost-processing File Reader XGBoost TreeEnsemble Learner Loop End

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