Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = FALSE,
comment = "#>",
fig.width = 7,
fig.height = 3.5,
warning = FALSE,
message = FALSE
)
## ----message=FALSE, warning=FALSE---------------------------------------------
library("DALEX")
library("ingredients")
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 = "Random Forest")
passanger <- titanic_imputed[sample(nrow(titanic_imputed), 1) ,-8]
passanger
## -----------------------------------------------------------------------------
importance_rf <- feature_importance(explain_titanic_rf)
plot(importance_rf)
## -----------------------------------------------------------------------------
describe(importance_rf)
## -----------------------------------------------------------------------------
perturbed_variable <- "class"
cp_rf <- ceteris_paribus(explain_titanic_rf,
passanger,
variables = perturbed_variable)
plot(cp_rf, variable_type = "categorical")
## -----------------------------------------------------------------------------
describe(cp_rf)
## -----------------------------------------------------------------------------
describe(cp_rf,
display_numbers = TRUE,
label = "the probability that the passanger will survive")
## -----------------------------------------------------------------------------
describe(cp_rf,
display_numbers = TRUE,
label = "the probability that the passanger will survive",
variables = perturbed_variable)
## -----------------------------------------------------------------------------
perturbed_variable_continuous <- "age"
cp_rf <- ceteris_paribus(explain_titanic_rf,
passanger)
plot(cp_rf, variables = perturbed_variable_continuous)
describe(cp_rf, variables = perturbed_variable_continuous)
## -----------------------------------------------------------------------------
pdp <- aggregate_profiles(cp_rf, type = "partial")
plot(pdp, variables = "fare")
describe(pdp, variables = "fare")
## -----------------------------------------------------------------------------
pdp <- aggregate_profiles(cp_rf, type = "partial", variable_type = "categorical")
plot(pdp, variables = perturbed_variable)
describe(pdp, variables = perturbed_variable)
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