plot.ceteris_paribus_explainer {ingredients} | R Documentation |
Plots Ceteris Paribus Profiles
Description
Function plot.ceteris_paribus_explainer
plots Individual Variable Profiles for selected observations.
Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs.
Find more details in Ceteris Paribus Chapter.
Usage
## S3 method for class 'ceteris_paribus_explainer'
plot(
x,
...,
size = 1,
alpha = 1,
color = "#46bac2",
variable_type = "numerical",
facet_ncol = NULL,
facet_scales = NULL,
variables = NULL,
title = "Ceteris Paribus profile",
subtitle = NULL,
categorical_type = "profiles"
)
Arguments
x |
a ceteris paribus explainer produced with function |
... |
other explainers that shall be plotted together |
size |
a numeric. Size of lines to be plotted |
alpha |
a numeric between |
color |
a character. Either name of a color or name of a variable that should be used for coloring |
variable_type |
a character. If |
facet_ncol |
number of columns for the |
facet_scales |
a character value for the |
variables |
if not |
title |
a character. Plot title. By default "Ceteris Paribus profile". |
subtitle |
a character. Plot subtitle. By default |
categorical_type |
a character. How categorical variables shall be plotted? Either |
Value
a ggplot2
object
References
Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/
Examples
library("DALEX")
model_titanic_glm <- glm(survived ~ gender + age + fare,
data = titanic_imputed, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
data = titanic_imputed[,-8],
y = titanic_imputed[,8],
verbose = FALSE)
cp_glm <- ceteris_paribus(explain_titanic_glm, titanic_imputed[1,])
cp_glm
plot(cp_glm, variables = "age")
library("ranger")
model_titanic_rf <- ranger(survived ~., data = titanic_imputed, probability = TRUE)
explain_titanic_rf <- explain(model_titanic_rf,
data = titanic_imputed[,-8],
y = titanic_imputed[,8],
label = "ranger forest",
verbose = FALSE)
selected_passangers <- select_sample(titanic_imputed, n = 100)
cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
cp_rf
plot(cp_rf, variables = "age") +
show_observations(cp_rf, variables = "age") +
show_rugs(cp_rf, variables = "age", color = "red")
selected_passangers <- select_sample(titanic_imputed, n = 1)
selected_passangers
cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
plot(cp_rf) +
show_observations(cp_rf)
plot(cp_rf, variables = "age") +
show_observations(cp_rf, variables = "age")
plot(cp_rf, variables = "class")
plot(cp_rf, variables = c("class", "embarked"), facet_ncol = 1)
plot(cp_rf, variables = c("class", "embarked"), facet_ncol = 1, categorical_type = "bars")
plotD3(cp_rf, variables = c("class", "embarked", "gender"),
variable_type = "categorical", scale_plot = TRUE,
label_margin = 70)