The workflow demonstrates how to use SHAP, Shapley Values and LIME implemenatations in KNIME 4.0 and generates a basic combined view.
It trains a Random Forest model for predicting survival of the Titanic dataset and compute explanations using those three different techniques.
The general steps demonstrated in this workflow are to:
1) Clean the data
2) Train the model
3) Take a sample row to explain
4) Run SHAP, Shapley Values and LIME
5) Combine the results in an interactive composite view.
Workflow
Model Interpretability, Titanic
External resources
- Christoph Molnar - Interpretable Machine Learning - A Guide for Making Black Box Models Explainable - 5.10 SHAP (SHapley Additive exPlanations)
- Christoph Molnar - Interpretable Machine Learning - A Guide for Making Black Box Models Explainable - 5.9 Shapley Values
- Christoph Molnar - Interpretable Machine Learning - A Guide for Making Black Box Models Explainable - 5.7 Local Surrogate (LIME)
Used extensions & nodes
Created with KNIME Analytics Platform version 4.1.1
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