# ceteris_paribus_2d: Ceteris Paribus 2D Plot In ingredients: Effects and Importances of Model Ingredients

 ceteris_paribus_2d 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 `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"))

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"))