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Shapley Values with H2O AutoML Example (ML Interpretability)

HTML 61.78% Jupyter Notebook 38.22%

h2oai h2o-automl h2o shapley interpretable-machine-learning

shapley-values-h2o-example's Introduction

Shapley Values with H2O AutoML Example (ML Interpretability)

Example using H2O's AutoML with the SHAP (SHapley Additive exPlanations) library

Task List

Classification Example
Regression Example
Use Plotly for visualizations

Notebooks

Filename	Description
shap_h2o_automl_classification.ipynb	jupyter notebook w/ classification examples
shap_h2o_automl_classification.html	html version for viewing force_plots
plotly_force_plot.ipynb	jupyter notebook plotly force-plots
plotly_force_plot.html	html version plotly force-plots

Step-by-step guide

[1]

conda env create -f environment.yml

[2]

source activate shap_h2o

[3] open jupyter notebook

References:

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shapley-values-h2o-example's Issues

how can shap be done for h2o model with categorical variables?

the model deal with categorical variables internally.
so the wrapper should somehow mimic the conversion of those variables to numeric, isn't it ?

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