importance_plot: Variable Importance Plot

In tlverse/sl3: Pipelines for Machine Learning and Super Learning

Variable Importance Plot

Description

Variable Importance Plot

Usage

importance_plot(x, nvar = min(30, nrow(x)))

Arguments

x	The two-column data.table returned by importance, where the first column is the covariate/groups and the second column is the importance score.
nvar	The maximum number of predictors to be plotted. Defaults to the minimum between 30 and the number of rows in x.

Value

A ggplot of variable importance.

Examples

# define ML task
data(cpp_imputed)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")

# build relatively fast learner library (not recommended for real analysis)
lasso_lrnr <- Lrnr_glmnet$new()
glm_lrnr <- Lrnr_glm$new()
ranger_lrnr <- Lrnr_ranger$new()
lrnrs <- c(lasso_lrnr, glm_lrnr, ranger_lrnr)
names(lrnrs) <- c("lasso", "glm", "ranger")
lrnr_stack <- make_learner(Stack, lrnrs)

# instantiate SL with default metalearner
sl <- Lrnr_sl$new(lrnr_stack)
sl_fit <- sl$train(task)
importance_result <- importance(sl_fit)
importance_plot(importance_result)

tlverse/sl3 documentation built on Nov. 18, 2024, 12:46 a.m.