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Computation of variable importance is splitted into one function that computes the importance and two that plots the results.

For example.

vi_results = pmb.compute_variable_importance(idata_bikes, μ_, X);

vi_results is a dictionary, we can do

pmb.plot_variable_importance(vi_results, X);

vi

and/or

pmb.plot_scatter_submodels(vi_results, grid=(2,2))

scatter

This last function needs more love, for instance, we could have error bars per observation, as we did for Friedman's example, and we need to improve labels, aesthetics, etc.

There is also a function to plot the variable inclusion

pmb.plot_variable_inclusion(idata_bikes, X)

inclusion

This could be used as a guide for setting the parameters for the backward_VI method

@aloctavodia aloctavodia merged commit 4ef2dd0 into main Nov 25, 2024
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@aloctavodia aloctavodia deleted the vip branch November 25, 2024 15:32
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