ingredients (version 2.3.0)

ceteris_paribus_2d: 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)

Value

an object of the class ceteris_paribus_2d_explainer.

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

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")

# \donttest{
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)
# }