calculate_oscillations: Calculate Oscillations for Ceteris Paribus Explainer In ingredients: Effects and Importances of Model Ingredients

Description

Oscillations are proxies for local feature importance at the instance level. Find more details in Ceteris Paribus Oscillations Chapter.

Usage

 1 calculate_oscillations(x, sort = TRUE, ...)

Arguments

 x a ceteris paribus explainer produced with the ceteris_paribus() function sort a logical value. If TRUE then rows are sorted along the oscillations ... other arguments

Value

an object of the class ceteris_paribus_oscillations

References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 library("DALEX") library("ingredients") titanic_small <- select_sample(titanic_imputed, n = 500, seed = 1313) # build a model model_titanic_glm <- glm(survived ~ gender + age + fare, data = titanic_small, family = "binomial") explain_titanic_glm <- explain(model_titanic_glm, data = titanic_small[,-8], y = titanic_small[,8]) cp_rf <- ceteris_paribus(explain_titanic_glm, titanic_small[1,]) calculate_oscillations(cp_rf) library("ranger") apartments_rf_model <- ranger(m2.price ~ construction.year + surface + floor + no.rooms + district, data = apartments) explainer_rf <- explain(apartments_rf_model, data = apartments_test[,-1], y = apartments_test\$m2.price, label = "ranger forest", verbose = FALSE) apartment <- apartments_test[1,] cp_rf <- ceteris_paribus(explainer_rf, apartment) calculate_oscillations(cp_rf)

ingredients documentation built on April 10, 2021, 5:06 p.m.