ceteris_paribus_2d {ingredients} | R Documentation |
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 |
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)
[Package ingredients version 2.3.0 Index]