flevr (version 0.0.4)

extract_importance_xgboost: Extract the learner-specific importance from an xgboost object

Description

Extract the individual-algorithm extrinsic importance from an xgboost object, along with the importance rank.

Usage

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

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)

Arguments

fit

the xgboost object.

feature_names

the feature names

coef

the Super Learner coefficient associated with the learner.

Examples

# \donttest{
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 <- as.matrix(dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))])
feature_nms <- names(x)
set.seed(20231129)
xgbmat <- xgboost::xgb.DMatrix(data = x, label = y)
# get the fit, using a small number of rounds for illustration only 
fit <- xgboost::xgboost(data = xgbmat, objective = "binary:logistic", nthread = 1, nrounds = 10)
# extract importance
importance <- extract_importance_xgboost(fit = fit, feature_names = feature_nms)
importance
# }