extract_importance_SL_learner {flevr}

Extract the learner-specific importance from a fitted SuperLearner algorithm

Description

Extract the individual-algorithm extrinsic importance from one fitted algorithm within the Super Learner, along with the importance rank.

Usage

extract_importance_SL_learner(fit = NULL, coef = 0, feature_names = "", ...)

Arguments

fit

the specific learner (e.g., from the Super Learner's fitLibrary list).

coef

the Super Learner coefficient associated with the learner.

feature_names

the feature names

...

other arguments to pass to algorithm-specific importance extractors.

Value

a tibble, with columns algorithm (the fitted algorithm), feature (the feature), importance (the algorithm-specific extrinsic importance of the feature), rank (the feature importance rank, with 1 indicating the most important feature), and weight (the algorithm's weight in the Super Learner)

Examples

data("biomarkers")
# subset to complete cases for illustration
cc <- complete.cases(biomarkers)
dat_cc <- biomarkers[cc, ]
# use only the mucinous outcome, not the high-malignancy outcome
y <- dat_cc$mucinous
x <- dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
# get the fit (using a simple library and 2 folds for illustration only)
library("SuperLearner")
set.seed(20231129)
fit <- SuperLearner::SuperLearner(Y = y, X = x, SL.library = c("SL.glm", "SL.mean"), 
                                  cvControl = list(V = 2))
# extract importance
importance <- extract_importance_SL_learner(fit = fit$fitLibrary[[1]]$object, 
                                            feature_names = feature_nms, coef = fit$coef[1])
importance