18.3 Local Explanations

• Provides information about a prediction for a single observation

• Which variables contribute to this result the most?

• “Break-down” explanations compute the contribution from each feature

  • Results for many explanatory variables can be presented in a limited space
  • Only the additive attributions, misleading for models with interactions

• Break-down plots with interactions
  • More accurate if the model itself uses interactions
  • Much more time-consuming
  • Interactions is not based on any formal statistical-significance test

• SHapley Additive exPlanations (SHAP) are based on “Shapley values”
  • “Cooperation is beneficial, because it may bring more benefit than individual actions”
  • Decompose a model’s predictions into contributions that can be attributed additively to different explanatory variables
  • If the model is not additive, then the Shapley values may be misleading

#Break-down
boost_breakdown <- predict_parts(explainer = explainer_boost,
                                 new_observation = sample_n(rush_df,1))

png(file="images/18_boost_breakdown.png", width = 600)
plot(boost_breakdown)
dev.off()


#Break-dwon Interactions
boost_breakdown2 <- predict_parts(explainer = explainer_boost,
                                 new_observation = sample_n(rush_df,1),
                                 type = "break_down_interactions")

png(file="images/18_boost_breakdown2.png", width = 600)
plot(boost_breakdown2)
dev.off()

#SHAP
boost_breakdown3 <- predict_parts(explainer = explainer_boost,
                                 new_observation = sample_n(rush_df,1),
                                 type = "shap")

png(file="images/18_boost_breakdown3.png", width = 600)
plot(boost_breakdown3)
dev.off()