SHAP and Variable Importance (Machine Learning)

ML models are powerful predictors but can be hard to interpret. This exercise uses H2O’s interpretation tools to understand why the gradient boosting model makes the predictions it does using plot_dataset.dta. Make sure your GBM model from the previous exercise is the most recently estimated H2O model (the h2omlgraph commands operate on the last h2oml model). If needed, re-estimate the GBM model.

1. Generate a variable importance plot, export it, and import it into Overleaf.

2. Generate partial dependence plots for the two most important predictors identified by the variable importance plot. Export them and import them into Overleaf.

3. Generate a SHAP summary plot, export it, and import it into Overleaf.

4. Examine the SHAP summary plot. For the top three variables, describe the relationship between the variable’s value (shading) and its SHAP value (horizontal position). For example: “Higher nitrogen application (darker dots) is associated with positive SHAP values, meaning it pushes yield predictions upward.”

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