ceteris_paribus_2d: Ceteris Paribus 2D Plot

In ingredients: Effects and Importances of Model Ingredients

Ceteris Paribus 2D Plot

Description

This function calculates ceteris paribus profiles for grid of values spanned by two variables. It may be useful to identify or present interactions between two variables.

Usage

ceteris_paribus_2d(explainer, observation, grid_points = 101, variables = NULL)

Arguments

explainer	a model to be explained, preprocessed by the DALEX::explain() function
observation	a new observation for which predictions need to be explained
grid_points	number of points used for response path. Will be used for both variables
variables	if specified, then only these variables will be explained

Value

an object of the class ceteris_paribus_2d_explainer.

References

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

Examples

library("DALEX")
library("ingredients")

model_titanic_glm <- glm(survived ~ age + fare,
                       data = titanic_imputed, family = "binomial")


explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed[,8])

cp_rf <- ceteris_paribus_2d(explain_titanic_glm, titanic_imputed[1,],
                       variables = c("age", "fare", "sibsp"))
head(cp_rf)

plot(cp_rf)

library("ranger")
set.seed(59)

apartments_rf_model <- ranger(m2.price ~., data = apartments)

explainer_rf <- explain(apartments_rf_model,
                        data = apartments_test[,-1],
                        y = apartments_test[,1],
                        label = "ranger forest",
                        verbose = FALSE)

new_apartment <- apartments_test[1,]
new_apartment

wi_rf_2d <- ceteris_paribus_2d(explainer_rf, observation = new_apartment,
                               variables = c("surface", "floor", "no.rooms"))
head(wi_rf_2d)
plot(wi_rf_2d)

ingredients documentation built on Jan. 15, 2023, 5:09 p.m.