I’ve developed a pymc model that is fairly accurate on the probability predictions given by pm.sample_posterior_predictive(idata)
. However I need a hint on explainability: How can I return back to the business actionable suggestions based on the features input to the model?
To better communicate the question I have run the same process on titanic survivor data, and can see a few important features from az.plot_trace()
which are centered to the left and right of 0. I conclude that these two features are important features to the predictions (shown here, it’s gender_classo
and boato
). How/what function can I use to say to the business in actual number ranges, “when boato is x value, that passenger is x% more likely to survive, all other features being equal?” I’ve tried finding the mean and std of these plots, and seems plausible - am I on the right track? Do I need the log of the mean?