ceteris_paribus_2d {ingredients} | R Documentation |
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.
ceteris_paribus_2d(explainer, observation, grid_points = 101, variables = NULL)
explainer |
a model to be explained, preprocessed by the |
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 |
an object of the class ceteris_paribus_2d_explainer
.
Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/
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)