R/extract_importance_svm.R
extract_importance_svm.RdExtract the individual-algorithm extrinsic importance from a glm object, along with the importance rank.
extract_importance_svm(
fit = NULL,
feature_names = "",
coef = 0,
x = NULL,
y = NULL
)the svm object.
the feature names
the Super Learner coefficient associated with the learner.
the features
the outcome
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)
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)
#> Warning: You are trying to do regression and your outcome only has two possible values Are you trying to do classification? If so, use a 2 level factor as your outcome column.
#> Loading required package: ggplot2
#> Loading required package: lattice
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: Variable(s) `' constant. Cannot scale data.
#> Warning: There were missing values in resampled performance measures.
importance
#> # A tibble: 22 × 5
#> algorithm feature importance rank weight
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 svm lab3_muc3ac_score 100 1 0
#> 2 svm lab1_actb 76.4 2 0
#> 3 svm lab6_ab_score 72.5 3 0
#> 4 svm lab1_telomerase_score 58.8 4 0
#> 5 svm lab4_glucose_score 57.7 5 0
#> 6 svm cea 45.7 6 0
#> 7 svm lab1_telomerase_neoplasia_call 45.6 7 0
#> 8 svm lab2_fluorescence_score 44.8 8 0
#> 9 svm lab4_areg_score 39.4 9 0
#> 10 svm lab1_molecules_score 30.6 10 0
#> # ℹ 12 more rows