Ceteris Paribus 2D Plot
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.