R/extract_importance_ranger.R
extract_importance_ranger.RdExtract the individual-algorithm extrinsic importance from a ranger object, along with the importance rank.
extract_importance_ranger(fit = NULL, feature_names = "", coef = 0)the ranger object.
the feature names
the Super Learner coefficient associated with the learner.
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 <- dat_cc[, !(names(dat_cc) %in% c("mucinous", "high_malignancy"))]
feature_nms <- names(x)
# get the fit
set.seed(20231129)
fit <- ranger::ranger(y ~ ., data = data.frame(y = y, x), importance = "impurity")
# extract importance
importance <- extract_importance_ranger(fit = fit, feature_names = feature_nms)
importance
#> # A tibble: 22 × 5
#> algorithm feature importance rank weight
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 rf lab3_muc3ac_score 4.07 1 0
#> 2 rf lab6_ab_score 3.71 2 0
#> 3 rf lab1_telomerase_score 3.69 3 0
#> 4 rf lab1_actb 3.58 4 0
#> 5 rf lab4_glucose_score 3.48 5 0
#> 6 rf cea 3.36 6 0
#> 7 rf lab2_fluorescence_score 3.18 7 0
#> 8 rf lab1_molecules_score 3.10 8 0
#> 9 rf lab3_muc5ac_score 2.99 9 0
#> 10 rf lab4_areg_score 2.98 10 0
#> # ℹ 12 more rows