extract_importance_svm {flevr}

Extract the learner-specific importance from an svm object

Description

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

Usage

extract_importance_svm(
  fit = NULL,
  feature_names = "",
  coef = 0,
  x = NULL,
  y = NULL
)

Arguments

fit

the svm object.

feature_names

the feature names

coef

the Super Learner coefficient associated with the learner.

x

the features

y

the outcome

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 <- as.data.frame(dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))])
x_mat <- as.matrix(x)
feature_nms <- names(x)
# get the fit 
set.seed(20231129)
fit <- kernlab::ksvm(x_mat, y)
# extract importance
importance <- extract_importance_svm(fit = fit, feature_names = feature_nms, x = x, y = y)
importance