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Model Interpretability: SHAP to explain predictions of Decision Tree
Titanic Prediction ExplanationsUsing SHAP to calculate feature importance of Titanic survival predictions on a decision tree model.General Steps1) Clean data2) Train model3) Sample row to explain4) Run SHAP5) Combine in composite view for display 4. Calculate explanations for predictions 5. Interactiveview3. Single rowto exmplain1. Clean data2. Train model Combined View Row Sampling Read andprepare data DecisionTree Learner Decision TreePredictor SHAP Loop Start SHAP Loop End Decision TreePredictor Format output Titanic Prediction ExplanationsUsing SHAP to calculate feature importance of Titanic survival predictions on a decision tree model.General Steps1) Clean data2) Train model3) Sample row to explain4) Run SHAP5) Combine in composite view for display 4. Calculate explanations for predictions 5. Interactiveview3. Single rowto exmplain1. Clean data2. Train modelCombined View Row Sampling Read andprepare data DecisionTree Learner Decision TreePredictor SHAP Loop Start SHAP Loop End Decision TreePredictor Format output

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Created by: Corey
Created at: 2019-06-20
On NodePit since: 2019-10-03
Last update: 2024-11-18
Created with KNIME version: v4.0.1
Tags: TitanicML InterpretabilityDecision TreeExplainabilitySHAP

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