extract_importance_glmnet {flevr}

Extract the learner-specific importance from a glmnet object

Description

Extract the individual-algorithm extrinsic importance from a glmnet object, along with the importance rank.

Usage

extract_importance_glmnet(fit = NULL, feature_names = "", coef = 0)

Arguments

fit

the glmnet or cv.glmnet object

feature_names

the feature names

coef

the Super Learner coefficient associated with the learner.

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 only 3 CV folds for illustration only)
set.seed(20231129)
fit <- glmnet::cv.glmnet(x = as.matrix(x), y = y, 
                         family = "binomial", nfolds = 3)
# extract importance
importance <- extract_importance_glmnet(fit = fit, feature_names = feature_nms)
importance