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03_​Titantic_​Prediction_​Explanations

Model Interpretability, Titanic
This workflows employs SHAP, Shapley Values, and LIME, for calculating the feature importance ofTitanic survival predictions on a random forest model. General Steps1) Clean data2) Train model3) Sample row to explain4) Run SHAP, Shapley Values and LIME5) Combine the results in an interactive composite view. removemissingto open the view Right click Component "Execute and Open Views"SHAP, Shapley Values,and LIME implementationsTitanic Datasingle rowto explain Random ForestLearner Column Rename Partitioning Column Filter Missing Value Combined View ML Interpretability Table Reader Row Sampling This workflows employs SHAP, Shapley Values, and LIME, for calculating the feature importance ofTitanic survival predictions on a random forest model. General Steps1) Clean data2) Train model3) Sample row to explain4) Run SHAP, Shapley Values and LIME5) Combine the results in an interactive composite view. removemissingto open the view Right click Component "Execute and Open Views"SHAP, Shapley Values,and LIME implementationsTitanic Datasingle rowto explainRandom ForestLearner Column Rename Partitioning Column Filter Missing Value Combined View ML Interpretability Table Reader Row Sampling

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Created by: Corey
Created at: 2019-06-20
On NodePit since: 2021-01-02
Last update: 2024-11-21
Created with KNIME version: v4.1.1
Tags: TitanicRandom ForestML InterpretabilityInteractive ViewLIMEShapley ValuesSHAPmachine learninginterpretability

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