plot.ceteris_paribus_explainer {ingredients} | R Documentation |
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.
## 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"
)
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 |
a ggplot2
object
Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/
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)